From df0b782f2f2a7a40031ecb618d889145dead59ad Mon Sep 17 00:00:00 2001 From: Karina Assini Date: Mon, 3 Nov 2025 13:43:17 -0300 Subject: [PATCH 1/4] first version - FT --- .github/workflows/pre-commit.yml | 14 + .gitignore | 22 +- .pre-commit-config.yaml | 44 + apps/whisper_fine_tuning/Makefile | 49 + apps/whisper_fine_tuning/README.md | 228 + .../deployment/endpoint/inference.py | 63 + .../deployment/endpoint/terraform/deploy.sh | 46 + .../endpoint/terraform/infer_example.json | 16 + .../deployment/endpoint/terraform/main.tf | 138 + .../deployment/endpoint/terraform/outputs.tf | 7 + .../endpoint/terraform/requirements.txt | 8 + .../deployment/endpoint/terraform/score.py | 80 + .../endpoint/terraform/terraform.tfstate | 43 + .../terraform/terraform.tfstate.backup | 43 + .../endpoint/terraform/test_deploy.sh | 143 + .../endpoint/terraform/variables.tf | 9 + .../deployment/endpoint/terraform/versions.tf | 10 + .../deployment/env_endpoint/DockerFile | 44 + .../deployment/env_endpoint/conda.yml | 29 + .../env_endpoint/docker-compose.yml | 10 + .../deployment/env_endpoint/environment.yml | 5 + .../deployment/register/README.md | 144 + .../deployment/register/register_model.py | 211 + .../deployment/training/environment.yml | 29 + .../deployment/training/training_job.yaml | 16 + .../docs/img/azure_logo.png | Bin 0 -> 295307 bytes .../docs/img/azure_workspace.png | Bin 0 -> 94522 bytes apps/whisper_fine_tuning/docs/img/image.png | Bin 0 -> 1210753 bytes apps/whisper_fine_tuning/docs/img/job.png | Bin 0 -> 222771 bytes apps/whisper_fine_tuning/docs/img/output.png | Bin 0 -> 207166 bytes .../infra/abbreviations.json | 145 + apps/whisper_fine_tuning/infra/main.bicep | 99 + .../whisper_fine_tuning/infra/main.bicepparam | 6 + .../module/ai-service-role-assignments.bicep | 41 + .../module/standard-dependent-resources.bicep | 133 + .../infra/module/standard-ml-workspace.bicep | 39 + .../infra/scripts/load_python_env.ps1 | 5 + .../infra/scripts/load_python_env.sh | 6 + .../infra/scripts/write_env.ps1 | 13 + .../infra/scripts/write_env.sh | 12 + .../notebooks/fine_tuned_usage.ipynb | 12267 ++++++++++++++++ apps/whisper_fine_tuning/poetry.lock | 7945 ++++++++++ apps/whisper_fine_tuning/pyproject.toml | 48 + apps/whisper_fine_tuning/src/__init__.py | 0 apps/whisper_fine_tuning/src/core/__init__.py | 0 .../src/core/data_prep/README.md | 218 + .../src/core/data_prep/load_data.py | 134 + .../src/core/data_prep/main_data_prep.py | 172 + .../core/data_prep/main_silver_data_prep.py | 168 + .../src/core/evaluation/evaluation_process.py | 248 + .../src/core/train/README.md | 67 + .../src/core/train/core/conda.yaml | 35 + .../src/core/train/core/config.py | 153 + .../src/core/train/core/train.py | 282 + .../src/core/train/core/train_nemo.py | 431 + .../src/core/train/main_train.py | 199 + .../src/core/train/main_train_nemo.py | 219 + .../whisper_fine_tuning/src/utils/__init__.py | 0 apps/whisper_fine_tuning/src/utils/utils.py | 18 + 59 files changed, 24553 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/pre-commit.yml create mode 100644 .pre-commit-config.yaml create mode 100644 apps/whisper_fine_tuning/Makefile create mode 100644 apps/whisper_fine_tuning/README.md create mode 100644 apps/whisper_fine_tuning/deployment/endpoint/inference.py create mode 100755 apps/whisper_fine_tuning/deployment/endpoint/terraform/deploy.sh create mode 100644 apps/whisper_fine_tuning/deployment/endpoint/terraform/infer_example.json create mode 100644 apps/whisper_fine_tuning/deployment/endpoint/terraform/main.tf create mode 100644 apps/whisper_fine_tuning/deployment/endpoint/terraform/outputs.tf create mode 100644 apps/whisper_fine_tuning/deployment/endpoint/terraform/requirements.txt create mode 100644 apps/whisper_fine_tuning/deployment/endpoint/terraform/score.py create mode 100644 apps/whisper_fine_tuning/deployment/endpoint/terraform/terraform.tfstate create mode 100644 apps/whisper_fine_tuning/deployment/endpoint/terraform/terraform.tfstate.backup create mode 100755 apps/whisper_fine_tuning/deployment/endpoint/terraform/test_deploy.sh create mode 100644 apps/whisper_fine_tuning/deployment/endpoint/terraform/variables.tf create mode 100644 apps/whisper_fine_tuning/deployment/endpoint/terraform/versions.tf create mode 100644 apps/whisper_fine_tuning/deployment/env_endpoint/DockerFile create mode 100644 apps/whisper_fine_tuning/deployment/env_endpoint/conda.yml create mode 100644 apps/whisper_fine_tuning/deployment/env_endpoint/docker-compose.yml create mode 100644 apps/whisper_fine_tuning/deployment/env_endpoint/environment.yml create mode 100644 apps/whisper_fine_tuning/deployment/register/README.md create mode 100644 apps/whisper_fine_tuning/deployment/register/register_model.py create mode 100644 apps/whisper_fine_tuning/deployment/training/environment.yml create mode 100644 apps/whisper_fine_tuning/deployment/training/training_job.yaml create mode 100644 apps/whisper_fine_tuning/docs/img/azure_logo.png create mode 100644 apps/whisper_fine_tuning/docs/img/azure_workspace.png create mode 100644 apps/whisper_fine_tuning/docs/img/image.png create mode 100644 apps/whisper_fine_tuning/docs/img/job.png create mode 100644 apps/whisper_fine_tuning/docs/img/output.png create mode 100644 apps/whisper_fine_tuning/infra/abbreviations.json create mode 100644 apps/whisper_fine_tuning/infra/main.bicep create mode 100644 apps/whisper_fine_tuning/infra/main.bicepparam create mode 100644 apps/whisper_fine_tuning/infra/module/ai-service-role-assignments.bicep create mode 100644 apps/whisper_fine_tuning/infra/module/standard-dependent-resources.bicep create mode 100644 apps/whisper_fine_tuning/infra/module/standard-ml-workspace.bicep create mode 100644 apps/whisper_fine_tuning/infra/scripts/load_python_env.ps1 create mode 100644 apps/whisper_fine_tuning/infra/scripts/load_python_env.sh create mode 100644 apps/whisper_fine_tuning/infra/scripts/write_env.ps1 create mode 100644 apps/whisper_fine_tuning/infra/scripts/write_env.sh create mode 100644 apps/whisper_fine_tuning/notebooks/fine_tuned_usage.ipynb create mode 100644 apps/whisper_fine_tuning/poetry.lock create mode 100644 apps/whisper_fine_tuning/pyproject.toml create mode 100644 apps/whisper_fine_tuning/src/__init__.py create mode 100644 apps/whisper_fine_tuning/src/core/__init__.py create mode 100644 apps/whisper_fine_tuning/src/core/data_prep/README.md create mode 100644 apps/whisper_fine_tuning/src/core/data_prep/load_data.py create mode 100644 apps/whisper_fine_tuning/src/core/data_prep/main_data_prep.py create mode 100644 apps/whisper_fine_tuning/src/core/data_prep/main_silver_data_prep.py create mode 100644 apps/whisper_fine_tuning/src/core/evaluation/evaluation_process.py create mode 100644 apps/whisper_fine_tuning/src/core/train/README.md create mode 100644 apps/whisper_fine_tuning/src/core/train/core/conda.yaml create mode 100644 apps/whisper_fine_tuning/src/core/train/core/config.py create mode 100644 apps/whisper_fine_tuning/src/core/train/core/train.py create mode 100644 apps/whisper_fine_tuning/src/core/train/core/train_nemo.py create mode 100644 apps/whisper_fine_tuning/src/core/train/main_train.py create mode 100644 apps/whisper_fine_tuning/src/core/train/main_train_nemo.py create mode 100644 apps/whisper_fine_tuning/src/utils/__init__.py create mode 100644 apps/whisper_fine_tuning/src/utils/utils.py diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml new file mode 100644 index 0000000..d172e4c --- /dev/null +++ b/.github/workflows/pre-commit.yml @@ -0,0 +1,14 @@ +name: pre-commit +on: [pull_request, push] +jobs: + pre-commit: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v4 + with: + python-version: '3.11' + - name: Install pre-commit + run: python -m pip install --upgrade pre-commit + - name: Run pre-commit + run: pre-commit run --all-files diff --git a/.gitignore b/.gitignore index 5561303..b3b1fe9 100644 --- a/.gitignore +++ b/.gitignore @@ -427,4 +427,24 @@ transliteration* .venv* infra/target* .vscode* -junk/ \ No newline at end of file +junk/ +*.code-workspace + +# Machine learning artifacts and cache directories +mlruns/ +custom_data/ +output_model_dir/ +nemo_rnnt_da/ +training_console.log +cache* +.mypy_cache/ +.ruff_cache/ + +# Local dataset directories +apps/whisper_fine_tuning/data/ + +*.DS_Store + +data/ + +predictions_dir/ \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..c1ea265 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,44 @@ +repos: + - repo: https://github.com/psf/black + rev: 24.10.0 + hooks: + - id: black + language_version: python3.11 # require Python 3.11 or newer + + - repo: https://github.com/charliermarsh/ruff-pre-commit + rev: v0.1.0 + hooks: + - id: ruff + args: [--fix, --extend-ignore, E402] # ruff will auto-fix many issues + additional_dependencies: [] + + - repo: https://github.com/pre-commit/mirrors-isort + rev: v5.10.1 + hooks: + - id: isort + args: ["--profile", "black"] + + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: end-of-file-fixer + - id: trailing-whitespace + - id: check-yaml + - id: check-added-large-files + + + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.6.1 # pick a valid tag you confirmed with git ls-remote + hooks: + - id: mypy + # keep commonly useful flags, then selectively disable error codes reported by mypy + args: + - --ignore-missing-imports + - --disable-error-code=import-untyped + - --disable-error-code=call-arg + - --disable-error-code=union-attr + - --disable-error-code=arg-type + - --disable-error-code=used-before-def + - --disable-error-code=attr-defined + files: \.py$ + language_version: python3.11 diff --git a/apps/whisper_fine_tuning/Makefile b/apps/whisper_fine_tuning/Makefile new file mode 100644 index 0000000..6ada71a --- /dev/null +++ b/apps/whisper_fine_tuning/Makefile @@ -0,0 +1,49 @@ +export HOME := $(HOME) +.ONESHELL: + +ifeq ($(OS),Windows_NT) + SHELL = cmd + CONDA_ACTIVATE = call %CONDA_PREFIX%\Scripts\activate.bat +else + SHELL = /bin/bash + CONDA_ACTIVATE = source $$(conda info --base)/etc/profile.d/conda.sh ; conda activate ; conda activate +endif + +setup_aml: + rm -rf ~/.pyenv + curl https://pyenv.run | bash + $(HOME)/.pyenv/bin/pyenv --version + $(HOME)/.pyenv/bin/pyenv install 3.12 --skip-existing + $(HOME)/.pyenv/bin/pyenv local 3.12 + python --version + conda create -n condav0 python=3.12 + $(CONDA_ACTIVATE) activate condav0 + conda install -c conda-forge poetry + poetry config virtualenvs.create true + poetry config virtualenvs.in-project true + poetry lock --no-update + poetry install + conda install pip + conda install -c conda-forge "ffmpeg>=5,<7" + sudo apt update && sudo apt install -y ffmpeg + python -m ipykernel install --user --name condav0 --display-name "condav0" + + +USERPROFILE := $(USERPROFILE) +CURRENT_DIR := $(shell cd) +setup_win: + if exist %USERPROFILE%\.pyenv rmdir /s /q %USERPROFILE%\.pyenv + git clone https://github.com/pyenv-win/pyenv-win.git "%USERPROFILE%\.pyenv" + $(USERPROFILE)\.pyenv\pyenv-win\bin\pyenv --version + $(USERPROFILE)\.pyenv\pyenv-win\bin\pyenv install 3.12 --skip-existing + $(USERPROFILE)\.pyenv\pyenv-win\bin\pyenv local 3.12 + python --version + python -m venv venv + echo $(CURRENT_DIR) + "$(CURRENT_DIR)/venv/Scripts/activate" + pip install poetry + poetry config virtualenvs.create true + poetry config virtualenvs.in-project true + poetry lock + poetry install + conda install pip diff --git a/apps/whisper_fine_tuning/README.md b/apps/whisper_fine_tuning/README.md new file mode 100644 index 0000000..d3327ac --- /dev/null +++ b/apps/whisper_fine_tuning/README.md @@ -0,0 +1,228 @@ +# Azure Logo Fine Tuning Open Source LLM Models - QLora and Lora features implemented + +## Overview +Open-source LLMs are powerful but require fine-tuning for specific tasks like chatbots or content generation. Fine-tuning these models can be expensive due to the need for substantial VRAM. For instance, fully fine-tuning the Llama7B model requires 112GB of VRAM. However, techniques like QLoRA and PEFT can significantly reduce these requirements. + +# Whisper Fine-Tuning Pipeline + +This repository provides a pipeline for fine-tuning OpenAI's Whisper models on custom audio datasets using LoRA (Low-Rank Adaptation) and PEFT (Parameter-Efficient Fine-Tuning). The solution is designed for scalable training and deployment on Azure Machine Learning. + +## Features +- Fine-tune Whisper models with LoRA and PEFT +- Custom data loading and preprocessing for speech datasets +- MLflow integration for experiment tracking and model logging (including all console logs as artifacts) +- Azure ML job specification for cloud training +- Modular codebase for easy extension + +## Directory Structure +``` +src/core/ + config.py # Training and LoRA configuration classes + load_data.py # Data loading and preprocessing + train.py # Trainer class and training logic +deployment/ + job_data.py # Data preparation script + job_train.py # Training script + environment.yml # Conda environment for Azure ML + training_job.yaml # Azure ML job specification +notebooks/ + fine_tuned_usage.ipynb # Example notebook for inference and usage +data/ + dataset_silver/ # Example processed dataset +``` + +## Setup +1. **Clone the repository** + ```bash + git clone + cd whisper-fine-tuning + ``` +2. **Prepare your dataset** + - Place your audio datasets in a directory (e.g., `data/train_raw`, `data/evaluation_raw`). + - Ensure datasets are in Hugging Face `datasets` format. + +3. **Create the environment** + +```bash +# Use Python 3.12 for the Poetry environment +poetry env use 3.12 + +# Activate the virtual environment +poetry shell + +# Install all project dependencies +poetry install +``` + +## Usage + +### Data Preparation + +### Data Preparation in Create Data + +For detailed instructions on dataset preparation, please refer to the README in the create_data directory. + + + +### Model Training + +Run the training pipeline: +```bash +python deployment/training/job_train.py --model_name openai/whisper-large-v2 --dataset ./data/silver/dataset --apply_lora True +``` + +If you want to load a lightweight model configuration, run the training command below. This command fine-tunes OpenAI's Whisper model with LoRA applied on your specified dataset: + +```bash +python deployment/training/job_train.py --model_name openai/whisper-large-v2 --dataset ./data/silver/dataset --apply_lora True --experiment_name "whisper-mayoruna-v2" + +``` + +This approach helps optimize resource usage while enabling effective model adaptation. + + +- All console and logging output will be saved as an MLflow artifact (`training_console.log`). + + +## Model Evaluating + + +```bash +python src/core/evaluation/evaluation_process.py --is_public_repo False --ckpt_dir "output_model_dir" --temp_ckpt_folder "temp" --eval_datasets data/raw/testing --device 0 --batch_size 16 --output_dir predictions_dir +``` + + +python deployment/training/job_train_nemo.py \ + --dataset_path data/silver/dataset \ + --output_dir nemo_rnnt_da/ \ + + +python deployment/training/job_train_nemo.py \ + --dataset_path data/silver/dataset \ + --output_dir output_model_dir/nemo_rnnt_da \ + --num_workers 0 + + +### Azure ML Training +1. **Configure Azure ML compute and workspace.** +2. **Submit the job:** + ```bash + az ml job create --file deployment/training_job.yaml + ``` + +## Arguments + +### `job_data.py` +- `--model_name`: Hugging Face model name (default: `openai/whisper-small`) +- `--train_datasets`: List of training dataset paths (required) +- `--eval_datasets`: List of evaluation dataset paths (optional) +- `--sampling_rate`: Audio sampling rate (default: 16000) +- `--num_proc`: Number of parallel jobs for data prep (default: 2) +- `--output_dir`: Output directory for processed dataset + +### `job_train.py` +- `--model_name`: Hugging Face model name (default: `openai/whisper-small`) +- `--dataset`: Path to processed dataset (required) +- `--output_dir`: Output directory for checkpoints (default: `output_model_dir`) +- `--apply_lora`: Whether to apply LoRA (default: True) +- `--language`: Language for adaptation (default: Hindi) +- `--sampling_rate`: Audio sampling rate (default: 16000) +- `--num_proc`: Number of parallel jobs for data prep (default: 2) +- `--train_strategy`: Training strategy (`steps` or `epoch`, default: `steps`) + +## Customization +- Modify `src/core/config.py` to adjust training and LoRA parameters. +- Update `deployment/environment.yml` to add/remove dependencies. +- Edit `deployment/training_job.yaml` for Azure ML compute, environment, and output settings. + +## Outputs +- Trained model checkpoints in the specified output directory +- MLflow experiment logs (including all console logs as artifacts) +- Optionally, model artifacts pushed to Hugging Face Hub + +## Inference Example + +See `notebooks/fine_tuned_usage.ipynb` for detailed usage. +To load and use a model logged with MLflow: +```python +import mlflow.transformers + +model_uri = "models:/whisper-fine-tuned/1" # or your own model URI +pipe = mlflow.transformers.load_model(model_uri) +result = pipe("path/to/audio/file.wav") +print(result["text"]) +``` +Or, using the pyfunc interface: +```python +import mlflow.pyfunc +import pandas as pd + +loaded_model = mlflow.pyfunc.load_model(model_uri) +df = pd.DataFrame({"inputs": ["path/to/audio/file.wav"]}) +result = loaded_model.predict(df) +print(result) +``` + + +## Steps to Configure Pre-commit + +1. Install pre-commit (if not already installed): + + ```bash + pip install pre-commit + ``` + +2. Use the `.pre-commit-config.yaml` file in the root of the repository + +3. Install the pre-commit hooks: + + ```bash + pre-commit install + ``` + +4. Run the hooks manually on all files to ensure consistency: + + ```bash + pre-commit run --all-files + ``` + +Integrating pre-commit into your workflow helps maintain consistent code standards and prevents common issues before they enter your codebase. + + +## Troubleshooting +- Ensure all dependencies in `environment.yml` are installed. +- Check dataset format and paths. +- Review Azure ML compute and environment configuration if running in the cloud. + +## License +This project is licensed under the MIT License. + +## Contact +For questions or support, please open an issue or contact the repository maintainer. + + +## Tips current scenario +| Parameter | Suggested | Reason | +|--------------------------------|------------------------|--------------------------------------------------------------------------------------------------| +| per_device_train_batch_size | 4–8 | With only 30 samples, large batches are unnecessary and may cause instability. | +| gradient_accumulation_steps | 1–4 | Increase if you want to simulate larger batches without memory overhead. | +| num_train_epochs | 10–30 | More epochs help the model learn from limited data. Use early stopping or monitor validation loss. | +| max_steps | Remove or set to -1 | Let training be driven by num_train_epochs to avoid premature stopping. | +| warmup_steps | 50–100 | 20k is too high for such a small dataset. | +| learning_rate | 1e-5 to 3e-5 | Your current value is reasonable, but monitor for overfitting. | +| eval_strategy | "epoch" | With few samples, evaluating per epoch is more meaningful. | +| save_strategy | "epoch" | Align with evaluation to save meaningful checkpoints. | +| fp16 | ✅ | Keep if your hardware supports it. | +| gradient_checkpointing | ✅ | Helps with memory efficiency. | +| optim | "adamw_bnb_8bit" | Good choice for memory-constrained environments. | +| load_best_model_at_end | ✅ | Helps select the best checkpoint. | +| predict_with_generate | ✅ | Needed for Whisper’s transcription tasks. | + +--- + +### 🧠 Additional Tips + +- **Use Data Augmentation**: Consider adding noise, pitch shift, or speed variation to artificially expand your dataset. +- **Freeze Most Layers**: Fine-tune only the final layers or adapters to reduce overfitting. +- **Use Early Stopping**: Monitor validation loss and stop training when it plateaus. +- **Log Carefully**: Avoid logging too many parameters to MLflow to prevent the `INVALID_PARAMETER_VALUE` error. diff --git a/apps/whisper_fine_tuning/deployment/endpoint/inference.py b/apps/whisper_fine_tuning/deployment/endpoint/inference.py new file mode 100644 index 0000000..9f0817b --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/endpoint/inference.py @@ -0,0 +1,63 @@ +import urllib.request +import json +from dotenv import load_dotenv +import os +import base64, json, os, pathlib, requests + + +# Request data goes here +# The example below assumes JSON formatting which may be updated +# depending on the format your endpoint expects. +# More information can be found here: +# https://docs.microsoft.com/azure/machine-learning/how-to-deploy-advanced-entry-script +load_dotenv() # Load environment variables from a .env file + +audio_path = pathlib.Path("/Users/karinaassiniandreatta/Documents/06 microsoft_codes/azuresamples/whisper-fine-tuning/src/core/create_data/matis/train_data/audio2.ogg") +audio_b64 = base64.b64encode(audio_path.read_bytes()).decode("utf-8") + +payload = { + "input_data": { + "columns": ["audio_base64"], + "index": [0], + "data": [[audio_b64]], + }, + "params": {}, +} +body = str.encode(json.dumps(payload)) + +url = os.getenv("SCORE_ENDPOINT", "https://ml-sandbox-core-hdydq.eastus2.inference.ml.azure.com/score") +# Replace this with the primary/secondary key, AMLToken, or Microsoft Entra ID token for the endpoint +api_key = os.getenv("API_KEY") +if not api_key: + raise Exception("A key should be provided to invoke the endpoint") + + +headers = {'Content-Type':'application/json', 'Accept': 'application/json', 'Authorization':('Bearer '+ api_key)} + +req = urllib.request.Request(url, body, headers) + +try: + response = urllib.request.urlopen(req) + raw_text = response.read().decode("utf-8") + payload = json.loads(raw_text) + + if isinstance(payload, str): + payload = json.loads(payload) + + if isinstance(payload, list) and payload: + for item in payload: + if isinstance(item, dict) and "transcription" in item: + print(item["transcription"]) + else: + print(item) + else: + print(payload) + +except urllib.error.HTTPError as error: + print("The request failed with status code: " + str(error.code)) + + # Print the headers - they include the requert ID and the timestamp, which are useful for debugging the failure + print(error.info()) + print(error.read().decode("utf8", 'ignore')) + + diff --git a/apps/whisper_fine_tuning/deployment/endpoint/terraform/deploy.sh b/apps/whisper_fine_tuning/deployment/endpoint/terraform/deploy.sh new file mode 100755 index 0000000..3ef9975 --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/endpoint/terraform/deploy.sh @@ -0,0 +1,46 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Check if terraform is installed; if not, install it +if ! command -v terraform &>/dev/null; then + echo "Terraform not found. Installing Terraform..." + TERRAFORM_VERSION="1.5.0" # Modify version as needed + TERRAFORM_ZIP="terraform_${TERRAFORM_VERSION}_linux_amd64.zip" + TEMP_DIR=$(mktemp -d) + wget "https://releases.hashicorp.com/terraform/${TERRAFORM_VERSION}/${TERRAFORM_ZIP}" -O "${TEMP_DIR}/${TERRAFORM_ZIP}" + unzip "${TEMP_DIR}/${TERRAFORM_ZIP}" -d "${TEMP_DIR}" + sudo mv "${TEMP_DIR}/terraform" /usr/local/bin/ + rm -rf "${TEMP_DIR}" + echo "Terraform installed successfully." +fi + +# Deploy wrapper: loads .env and exports TF_VAR_ variables for Terraform +# Usage: place a .env in this folder (or edit .env.sample), then run ./deploy.sh + +ENV_FILE=".env" +if [[ -f "$ENV_FILE" ]]; then + echo "Loading $ENV_FILE and exporting TF_VAR_* variables for Terraform" + while IFS='=' read -r raw_key raw_value || [[ -n "$raw_key" ]]; do + key="$raw_key" + value="$raw_value" + # skip comments and empty lines + [[ -z "${key// /}" ]] && continue + [[ "$key" =~ ^\s*# ]] && continue + # trim spaces + key=$(echo "$key" | sed 's/^[[:space:]]*//;s/[[:space:]]*$//') + value=$(echo "$value" | sed 's/^[[:space:]]*//;s/[[:space:]]*$//') + # remove surrounding quotes if any + value=$(echo "$value" | sed 's/^"\(.*\)"$/\1/; s/^'\''\(.*\)'\''$/\1/') + # lowercase the key for TF_VAR convention + lc_key=$(echo "$key" | tr '[:upper:]' '[:lower:]') + export_var_name="TF_VAR_${lc_key}" + export ${export_var_name}="$value" + echo "Exported ${export_var_name}='${value}'" + done < "$ENV_FILE" +else + echo "No $ENV_FILE found. Create one or pass -var flags to terraform." +fi + +echo "Running terraform init && apply" +terraform init +terraform apply -auto-approve "$@" diff --git a/apps/whisper_fine_tuning/deployment/endpoint/terraform/infer_example.json b/apps/whisper_fine_tuning/deployment/endpoint/terraform/infer_example.json new file mode 100644 index 0000000..50ca202 --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/endpoint/terraform/infer_example.json @@ -0,0 +1,16 @@ +{ + "input_data": { + "columns": [ + "input" + ], + "index": [ + 0 + ], + "data": [ + [ + "https://www.soundhelix.com/examples/mp3/SoundHelix-Song-1.mp3" + ] + ] + }, + "params": {} +} diff --git a/apps/whisper_fine_tuning/deployment/endpoint/terraform/main.tf b/apps/whisper_fine_tuning/deployment/endpoint/terraform/main.tf new file mode 100644 index 0000000..0491018 --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/endpoint/terraform/main.tf @@ -0,0 +1,138 @@ +variable "resource_group" { + description = "Azure resource group where the ML workspace resides" + type = string +} + +variable "workspace_name" { + description = "Azure ML workspace name" + type = string +} + +variable "location" { + description = "Azure region for the endpoint" + type = string + default = "eastus" +} + +variable "endpoint_name" { + description = "Name for the Azure ML online endpoint" + type = string +} + +variable "model_name" { + description = "Name of the model registered in MLflow (Azure ML)" + type = string +} + +variable "model_version" { + description = "Version of the model to deploy" + type = string + default = "1" +} + +variable "environment_name" { + description = "Azure ML environment name used to build the container" + type = string +} + +variable "deployment_name" { + description = "Name for the deployment (traffic target)" + type = string + default = "blue" +} + +variable "cpu_cores" { + type = number + default = 1 +} + +variable "memory_gb" { + type = number + default = 4 +} + +resource "null_resource" "create_endpoint" { + # Re-run the provisioner when any of these inputs change + triggers = { + endpoint = var.endpoint_name + deployment = var.deployment_name + model = "${var.model_name}:${var.model_version}" + environment = var.environment_name + resources = "${var.cpu_cores}-${var.memory_gb}" + } + + provisioner "local-exec" { + # Use set -ex so failures are shown and commands are printed for debugging + command = < /dev/null + +if az extension show -n azure-cli-ml >/dev/null 2>&1; then + echo "WARNING: extension 'azure-cli-ml' is installed alongside 'ml'. This can cause conflicts. Consider running: az extension remove -n azure-cli-ml" +fi + +# Create endpoint YAML and apply (create or update) +cat > /tmp/endpoint_${var.endpoint_name}.yml < /tmp/deployment_${var.deployment_name}.yml </dev/null 2>&1; then + echo "ERROR: Model '${var.model_name}' version '${var.model_version}' not found in workspace ${var.workspace_name} (RG ${var.resource_group})." + echo "Register the model or correct MODEL_NAME/MODEL_VERSION in your .env or TF variables." + exit 1 +fi + +# Validate environment exists (warn only) +# environment variable is emitted as azureml::; strip azureml: if present +env_ref="${var.environment_name}" +# Use portable parsing to split name:version +env_check_name=$(printf "%s" "$${env_ref}" | cut -d':' -f1) +env_check_version=$(printf "%s" "$${env_ref}" | cut -d':' -f2-) +if ! az ml environment show --name "$${env_check_name}" --version "$${env_check_version}" --resource-group ${var.resource_group} --workspace-name ${var.workspace_name} >/dev/null 2>&1; then + echo "WARNING: Environment '${var.environment_name}' not found as a registered environment in workspace ${var.workspace_name}." + echo "Ensure the environment is registered (azureml::) or use a valid environment reference." +fi + +az ml online-deployment create --file /tmp/deployment_${var.deployment_name}.yml --resource-group ${var.resource_group} --workspace-name ${var.workspace_name} || \ +az ml online-deployment update --file /tmp/deployment_${var.deployment_name}.yml --name ${var.deployment_name} --endpoint-name ${var.endpoint_name} --resource-group ${var.resource_group} --workspace-name ${var.workspace_name} + +echo "Deployment applied. You may need to adjust traffic or default deployment using the Azure CLI version you have installed." +EOT + } +} diff --git a/apps/whisper_fine_tuning/deployment/endpoint/terraform/outputs.tf b/apps/whisper_fine_tuning/deployment/endpoint/terraform/outputs.tf new file mode 100644 index 0000000..f08862f --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/endpoint/terraform/outputs.tf @@ -0,0 +1,7 @@ +output "endpoint_name" { + value = var.endpoint_name +} + +output "deployment_name" { + value = var.deployment_name +} diff --git a/apps/whisper_fine_tuning/deployment/endpoint/terraform/requirements.txt b/apps/whisper_fine_tuning/deployment/endpoint/terraform/requirements.txt new file mode 100644 index 0000000..75e9a92 --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/endpoint/terraform/requirements.txt @@ -0,0 +1,8 @@ +fastapi +uvicorn[standard] +mlflow +pandas +requests +soundfile +numpy +python-multipart diff --git a/apps/whisper_fine_tuning/deployment/endpoint/terraform/score.py b/apps/whisper_fine_tuning/deployment/endpoint/terraform/score.py new file mode 100644 index 0000000..4ce4a3a --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/endpoint/terraform/score.py @@ -0,0 +1,80 @@ +import base64 +import json +import os +from pathlib import Path + +import mlflow +import torch + +def init(): + global asr_pipeline, device, forced_decoder_ids + + model_dir = Path(os.getenv("AZUREML_MODEL_DIR", ".")) + model_uri = model_dir / "model" + if not model_uri.exists(): + raise FileNotFoundError(f"MLflow model directory not found at {model_uri}") + + asr_pipeline = mlflow.transformers.load_model(str(model_uri)) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + try: + asr_pipeline.to(device) + except AttributeError: + if hasattr(asr_pipeline, "model"): + asr_pipeline.model.to(device) + asr_pipeline.model.eval() + + + forced_decoder_ids = asr_pipeline.tokenizer.get_decoder_prompt_ids( + language="portuguese", + task="transcribe", + ) + + print("Whisper pipeline loaded via MLflow and ready.") + +def run(raw_data): + try: + # Azure ML sends input_data as a DataFrame in split orientation + data = json.loads(raw_data) + # Support both DataFrame and record input + if "input_data" in data: + if isinstance(data["input_data"], dict) and "data" in data["input_data"]: + # DataFrame split orientation + audio_b64_list = [row[0] for row in data["input_data"]["data"]] + elif isinstance(data["input_data"], list): + # List of dicts + audio_b64_list = [row.get("audio_base64") for row in data["input_data"]] + else: + raise ValueError("Unsupported input_data format") + else: + raise ValueError("Missing input_data in request") + + results = [] + for audio_b64 in audio_b64_list: + if not audio_b64: + results.append({"error": "Empty audio_base64"}) + continue + audio_bytes = base64.b64decode(audio_b64) + # Convert bytes to numpy array (float32 PCM, 16kHz expected for Whisper) + import numpy as np + import io + import soundfile as sf + audio_array, _ = sf.read(io.BytesIO(audio_bytes), dtype="float32") + generate_kwargs = { + "repetition_penalty": 1.2, + "no_repeat_ngram_size": 3, + "max_new_tokens": 128, + "do_sample": False, + "forced_decoder_ids": forced_decoder_ids + } + pipeline_inputs = {"array": audio_array, "sampling_rate": 16000} + + transcription = asr_pipeline( + pipeline_inputs, + generate_kwargs=generate_kwargs, + batch_size=1, + )["text"] + results.append({"transcription": transcription}) + return json.dumps(results) + except Exception as e: + return json.dumps({"error": str(e)}) \ No newline at end of file diff --git a/apps/whisper_fine_tuning/deployment/endpoint/terraform/terraform.tfstate b/apps/whisper_fine_tuning/deployment/endpoint/terraform/terraform.tfstate new file mode 100644 index 0000000..a51daa5 --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/endpoint/terraform/terraform.tfstate @@ -0,0 +1,43 @@ +{ + "version": 4, + "terraform_version": "1.13.1", + "serial": 4, + "lineage": "c55b2916-818a-288d-7bf3-7d2fce00f321", + "outputs": { + "deployment_name": { + "value": "blue", + "type": "string" + }, + "endpoint_name": { + "value": "whisper-edp-v2", + "type": "string" + } + }, + "resources": [ + { + "mode": "managed", + "type": "null_resource", + "name": "create_endpoint", + "provider": "provider[\"registry.terraform.io/hashicorp/null\"]", + "instances": [ + { + "status": "tainted", + "schema_version": 0, + "attributes": { + "id": "448760079048018103", + "triggers": { + "deployment": "blue", + "endpoint": "whisper-edp-v2", + "environment": "vllm:6", + "model": "whisper-fine-tuned:2", + "resources": "1-4" + } + }, + "sensitive_attributes": [], + "identity_schema_version": 0 + } + ] + } + ], + "check_results": null +} diff --git a/apps/whisper_fine_tuning/deployment/endpoint/terraform/terraform.tfstate.backup b/apps/whisper_fine_tuning/deployment/endpoint/terraform/terraform.tfstate.backup new file mode 100644 index 0000000..69376cf --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/endpoint/terraform/terraform.tfstate.backup @@ -0,0 +1,43 @@ +{ + "version": 4, + "terraform_version": "1.13.1", + "serial": 1, + "lineage": "c55b2916-818a-288d-7bf3-7d2fce00f321", + "outputs": { + "deployment_name": { + "value": "blue", + "type": "string" + }, + "endpoint_name": { + "value": "whisper-edp-v2", + "type": "string" + } + }, + "resources": [ + { + "mode": "managed", + "type": "null_resource", + "name": "create_endpoint", + "provider": "provider[\"registry.terraform.io/hashicorp/null\"]", + "instances": [ + { + "status": "tainted", + "schema_version": 0, + "attributes": { + "id": "3922989188787705048", + "triggers": { + "deployment": "blue", + "endpoint": "whisper-edp-v2", + "environment": "vllm:6", + "model": "whisper-fine-tuned:2", + "resources": "1-4" + } + }, + "sensitive_attributes": [], + "identity_schema_version": 0 + } + ] + } + ], + "check_results": null +} diff --git a/apps/whisper_fine_tuning/deployment/endpoint/terraform/test_deploy.sh b/apps/whisper_fine_tuning/deployment/endpoint/terraform/test_deploy.sh new file mode 100755 index 0000000..a15c6e4 --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/endpoint/terraform/test_deploy.sh @@ -0,0 +1,143 @@ +#!/usr/bin/env bash +set -euo pipefail + +# test_deploy.sh +# Test an Azure ML managed online endpoint or a direct scoring URI by sending audio. +# +# Usage: +# ./test_deploy.sh --file path/to/example.wav [--endpoint-name NAME --resource-group RG --workspace-name WS] +# or set the env vars ENDPOINT_NAME, RESOURCE_GROUP, WORKSPACE_NAME +# or set SCORING_URI and the script will call it directly using AAD token fetched from az +# +# The script builds a JSON payload {"audio_base64": ""} and then calls +# - az ml online-endpoint invoke (preferred if az ml extension present) +# - otherwise falls back to curl against SCORING_URI with an AAD access token + +# ./test_deploy.sh --endpoint-name whisper-edp-v1 --file "/Users/karinaassiniandreatta/Documents/06 microsoft_codes/azuresamples/whisper-fine-tuning/src/core/create_data/test_data/audio31.opus" +#./test_deploy.sh --endpoint-name whisper-edp-v1 --resource-group KA-SAND-RG --workspace-name ml-sandbox-core --file "/Users/karinaassiniandreatta/Documents/06 microsoft_codes/azuresamples/whisper-fine-tuning/src/core/create_data/test_data/audio31.opus" + +usage() { + cat < [--endpoint-name NAME --resource-group RG --workspace-name WS] + +Options (any can be set via env): + --file PATH WAV file to send (required) + --endpoint-name NAME Azure ML endpoint name (optional) + --resource-group RG Resource group (optional) + --workspace-name WS Workspace name (optional) + --scoring-uri URI Direct scoring URI (optional, env SCORING_URI) + --request-file FILE Save generated request JSON to FILE (optional) + --help Show this help + +Examples: + ENDPOINT_NAME=my-endp RESOURCE_GROUP=my-rg WORKSPACE_NAME=my-ws ./test_deploy.sh --file sample.wav + SCORING_URI=https://.../score ./test_deploy.sh --file sample.wav +EOF +} + +AUDIO_FILE="" +ENDPOINT_NAME="${ENDPOINT_NAME:-}" +RESOURCE_GROUP="${RESOURCE_GROUP:-}" +WORKSPACE_NAME="${WORKSPACE_NAME:-}" +SCORING_URI="${SCORING_URI:-}" +REQUEST_FILE="" + +while [[ $# -gt 0 ]]; do + case "$1" in + --file) AUDIO_FILE="$2"; shift 2;; + --endpoint-name) ENDPOINT_NAME="$2"; shift 2;; + --resource-group) RESOURCE_GROUP="$2"; shift 2;; + --workspace-name) WORKSPACE_NAME="$2"; shift 2;; + --scoring-uri) SCORING_URI="$2"; shift 2;; + --request-file) REQUEST_FILE="$2"; shift 2;; + --help) usage; exit 0;; + *) echo "Unknown arg: $1"; usage; exit 1;; + esac +done + +if [[ -z "$AUDIO_FILE" ]]; then + echo "Error: --file is required" + usage + exit 2 +fi + +if [[ ! -f "$AUDIO_FILE" ]]; then + echo "Error: file not found: $AUDIO_FILE" + exit 3 +fi + +#TMPREQ=$(mktemp --suffix .json) +TMPREQ=$(mktemp /tmp/tmpreq.XXXXXX.json) +trap 'rm -f "$TMPREQ"' EXIT + +# base64 encode the audio (no line breaks) +if command -v base64 &>/dev/null; then + # macOS base64 does not support -w, so use input redirection and remove newlines + AUDIO_B64=$(base64 < "$AUDIO_FILE" | tr -d '\n') +else + # fallback to openssl if base64 is not available + AUDIO_B64=$(openssl base64 -A < "$AUDIO_FILE") +fi + +#jq -n '{input_data: {columns: ["input"], index: [0], data: [["/Users/karinaassiniandreatta/Documents/06 microsoft_codes/azuresamples/whisper-fine-tuning/src/core/create_data/test_data/audio31.opus"]]}, params: {}}' > "$TMPREQ" + +jq -n '{input_data: {columns: ["input"], index: [0], data: [["https://www.soundhelix.com/examples/mp3/SoundHelix-Song-1.mp3"]]}, params: {}}' > "$TMPREQ" + +if [[ -n "$REQUEST_FILE" ]]; then + cp "$TMPREQ" "$REQUEST_FILE" + echo "Saved request JSON to $REQUEST_FILE" +fi + + +echo "Request JSON (full):" +cat "$TMPREQ" +echo + +echo +# Prefer az ml online-endpoint invoke if available +if command -v az &>/dev/null && az extension show -n ml &>/dev/null 2>&1; then + if [[ -n "$ENDPOINT_NAME" && -n "$RESOURCE_GROUP" && -n "$WORKSPACE_NAME" ]]; then + echo "Invoking endpoint via az ml online-endpoint invoke: $ENDPOINT_NAME" + az ml online-endpoint invoke --name "$ENDPOINT_NAME" --resource-group "$RESOURCE_GROUP" --workspace-name "$WORKSPACE_NAME" --request-file "$TMPREQ" || { + echo "az ml invoke failed" + exit 4 + } + exit 0 + else + echo "az ml extension present but endpoint/workspace args missing; skipping az ml invoke" + fi +fi + +# Fallback: require SCORING_URI +if [[ -z "$SCORING_URI" ]]; then + echo "SCORING_URI not provided and az ml invoke unavailable. Provide SCORING_URI env or set ENDPOINT_NAME/RESOURCE_GROUP/WORKSPACE_NAME for az invocation." + exit 5 +fi + +# Acquire access token for management resource +if ! command -v az &>/dev/null; then + echo "az CLI not found; cannot acquire AAD token for direct curl. Install az or use az ml invoke." >&2 + exit 6 +fi + +TOKEN=$(az account get-access-token --resource https://management.azure.com/ --query accessToken -o tsv) +if [[ -z "$TOKEN" ]]; then + echo "Failed to acquire access token via az" >&2 + exit 7 +fi + +echo "Calling SCORING_URI: $SCORING_URI" +HTTP_RESP=$(curl -sS -X POST "$SCORING_URI" \ + -H "Authorization: Bearer $TOKEN" \ + -H "Content-Type: application/json" \ + --data-binary "@$TMPREQ") || true + +if [[ -z "$HTTP_RESP" ]]; then + echo "No response (empty)." + exit 8 +fi + +echo "Response:" +echo "$HTTP_RESP" | jq || echo "$HTTP_RESP" + +exit 0 diff --git a/apps/whisper_fine_tuning/deployment/endpoint/terraform/variables.tf b/apps/whisper_fine_tuning/deployment/endpoint/terraform/variables.tf new file mode 100644 index 0000000..def566b --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/endpoint/terraform/variables.tf @@ -0,0 +1,9 @@ +// This file intentionally left as a usage hint only. +// Variable declarations live in `main.tf` for this lightweight wrapper. +// Example usage: +// terraform apply \ +// -var "resource_group=my-rg" \ +// -var "workspace_name=my-ws" \ +// -var "endpoint_name=my-endpoint" \ +// -var "model_name=my-model" \ +// -var "environment_name=my-env" diff --git a/apps/whisper_fine_tuning/deployment/endpoint/terraform/versions.tf b/apps/whisper_fine_tuning/deployment/endpoint/terraform/versions.tf new file mode 100644 index 0000000..8060c0f --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/endpoint/terraform/versions.tf @@ -0,0 +1,10 @@ +terraform { + required_version = ">= 1.1.0" + + required_providers { + null = { + source = "hashicorp/null" + version = ">= 3.0.0" + } + } +} diff --git a/apps/whisper_fine_tuning/deployment/env_endpoint/DockerFile b/apps/whisper_fine_tuning/deployment/env_endpoint/DockerFile new file mode 100644 index 0000000..248a741 --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/env_endpoint/DockerFile @@ -0,0 +1,44 @@ +FROM vllm/vllm-openai:latest + +# Copy environment file (it lives in the same build context). Avoid copying parent paths. +# The environment build context configured in Azure ML should point to this folder so +# `environment.yml` is available at build time. +COPY conda.yml /tmp/conda.yml + +# Instalar Miniconda +RUN apt-get update && apt-get install -y wget bzip2 && \ + wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh && \ + bash miniconda.sh -b -p /opt/conda && \ + rm miniconda.sh && \ + /opt/conda/bin/conda clean -afy + + +# Colocar conda no PATH +ENV PATH=/opt/conda/bin:$PATH + +# Update the base conda environment using your environment file (optional). +# You can remove this step if the base image already includes required deps. + +RUN conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main && \ + conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r && \ + conda env update --file /tmp/conda.yml --name base && \ + conda clean -afy + + +# Install ffmpeg if the base image doesn't provide it +RUN conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main && \ + conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r && \ + conda install -c conda-forge "ffmpeg>=5,<7" -y && \ + conda clean -afy + +# MODEL_NAME can be provided as a build-arg (for build-time) or as a runtime env var. +# Avoid embedding secrets at build time. Prefer passing non-secret values at runtime. +ARG MODEL_NAME="" +# If AZUREML_MODEL_DIR is set by the platform at runtime, prefer combining at runtime. +ENV MODEL_NAME="${MODEL_NAME}" + +RUN echo "MODEL_NAME is $MODEL_NAME" + +# Entrypoint: the Azure ML runtime exposes AZUREML_MODEL_DIR at runtime when mounting model assets. +# We construct the effective model path at container start so the same image works locally and in AML. +CMD ["/bin/bash", "-lc", "MODEL_PATH=\"${AZUREML_MODEL_DIR:-}\${MODEL_NAME}\"; python3 -m vllm.entrypoints.openai.api_server --model \"$MODEL_PATH\" $VLLM_ARGS"] diff --git a/apps/whisper_fine_tuning/deployment/env_endpoint/conda.yml b/apps/whisper_fine_tuning/deployment/env_endpoint/conda.yml new file mode 100644 index 0000000..4fff3cc --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/env_endpoint/conda.yml @@ -0,0 +1,29 @@ +name: whisper-fine-tuning +channels: + - conda-forge + - defaults +dependencies: + - python=3.10 + - pip + - numpy + - pandas + - scikit-learn + - pytorch + - torchaudio + - ffmpeg + - pip: + - ffmpeg-python + - transformers + - datasets + - peft + - mlflow + - accelerate + - bitsandbytes + - soundfile + - huggingface_hub + - evaluate + - gradio + - sentencepiece + - protobuf + - typing-extensions + - torchcodec diff --git a/apps/whisper_fine_tuning/deployment/env_endpoint/docker-compose.yml b/apps/whisper_fine_tuning/deployment/env_endpoint/docker-compose.yml new file mode 100644 index 0000000..fcfeb43 --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/env_endpoint/docker-compose.yml @@ -0,0 +1,10 @@ +services: + web: + build: + context: . + args: + - MODEL_NAME + env_file: + - .env + ports: + - "8080:8080" diff --git a/apps/whisper_fine_tuning/deployment/env_endpoint/environment.yml b/apps/whisper_fine_tuning/deployment/env_endpoint/environment.yml new file mode 100644 index 0000000..cc0d97f --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/env_endpoint/environment.yml @@ -0,0 +1,5 @@ +$schema: https://azuremlschemas.azureedge.net/latest/environment.schema.json +name: vllm +build: + path: . + dockerfile_path: DockerFile diff --git a/apps/whisper_fine_tuning/deployment/register/README.md b/apps/whisper_fine_tuning/deployment/register/README.md new file mode 100644 index 0000000..3dd7aad --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/register/README.md @@ -0,0 +1,144 @@ +# Environment Setup + +Before proceeding, ensure you are logged in with the Azure CLI using `az login`. + +1. Set your Azure subscription: + ```bash + az account set --subscription "" + ``` + +2. Configure default workspace and resource group: + ```bash + az configure --defaults workspace="" group="" + ``` + +3. Create the Azure ML environment: + ```bash + az ml environment create -f deployment/env_endpoint/environment.yml + ``` + +4. Define your environment variables: + Create or update the `.env` file located in the `deployment/endpoint` directory with the required configuration values. + + +## Model Registration (`training/register_model.py`) + +After training, use this script to register the latest model from an experiment into MLflow Model Registry: + +```bash +# Register with auto-generated model name +python deployment/training/register_model.py \ + --experiment_name "whisper-gujarati-whisper-large-v2-lora-20251015" + +# Register with custom model name and set to Production +python deployment/training/register_model.py \ + --experiment_name "whisper-gujarati-whisper-large-v2-lora-20251015" \ + --model_name "whisper-gujarati-production" \ + --stage "Production" + +# Register both main model and PEFT adapter +python deployment/register/register_model.py --experiment_name "whisper-matis-v1" --model_name "whisper-matis-v1" --artifact_path model --also_register_adapter True +``` + +**What the registration script does:** +1. **Finds the latest run** from the specified MLflow experiment +2. **Registers the model** in MLflow Model Registry with versioning +3. **Sets model stage** (None, Staging, Production, Archived) +4. **Optionally registers PEFT adapters** as separate models +5. **Auto-generates model names** based on experiment name if not provided + +**Arguments:** +- `--experiment_name` (required): Name of the MLflow experiment +- `--model_name` (optional): Custom model name for registration +- `--artifact_path` (default: "model"): Path to model artifact in the run +- `--stage` (default: "Staging"): Model stage to set +- `--also_register_adapter`: Flag to also register the PEFT adapter + +**After registration, use the model:** +```python +import mlflow + +# Load registered model for inference +model_uri = "models:/whisper-gujarati-v1/1" # or /latest +pipe = mlflow.transformers.load_model(model_uri) +result = pipe("path/to/audio.wav") +print(result["text"]) +``` + + +# Terraform wrapper for Azure ML online endpoint + +This folder contains a lightweight Terraform wrapper and helper script to create or update an Azure Machine Learning Online Endpoint and a deployment that points to a model registered in the workspace (MLflow). It uses the Azure CLI `az ml` commands via a `null_resource` local-exec. + +Prerequisites +- Install Terraform (>= 1.1.0) +- Install Azure CLI and login: `az login` +- Install the Azure ML extension: `az extension add -n ml` +- Ensure the target Azure ML workspace, resource group, and model (registered via MLflow / Azure ML) exist + +Files in this folder +- `main.tf`, `variables.tf`, `outputs.tf`, `versions.tf` - Terraform files +- `deploy.sh` - helper script that reads `.env`, exports `TF_VAR_*` variables (lowercased keys) and runs `terraform init && terraform apply` +- `.env.sample` - example variables for local testing + +Quick local test (recommended) + +1. Copy the sample .env and edit: + +```bash +cp .env.sample .env +# edit .env and set RESOURCE_GROUP, WORKSPACE_NAME, ENDPOINT_NAME, MODEL_NAME, ENVIRONMENT_NAME, etc. +``` + +2. Make the wrapper executable and run it: + +```bash +chmod +x deploy.sh +./deploy.sh +``` + +The script will load `.env`, export `TF_VAR_*` variables, then run `terraform init` and `terraform apply -auto-approve`. + +Direct Terraform usage + +You can also run Terraform directly and pass variables from `.env` by converting to `terraform.tfvars` or exporting `TF_VAR_` variables. + +- Convert `.env` to `terraform.tfvars` (simple): + +```bash +sed -E 's/^([A-Za-z0-9_]+)=(.*)$/\L\1\E = "\2"/' .env > terraform.tfvars +terraform init +terraform apply -auto-approve +``` + +- Or export TF_VAR_ variables (example): + +```bash +export TF_VAR_resource_group="my-rg" +export TF_VAR_workspace_name="my-ws" +export TF_VAR_endpoint_name="my-endpoint" +export TF_VAR_model_name="my-model" +export TF_VAR_environment_name="my-env" +terraform init +terraform apply -auto-approve +``` + +Notes and recommendations +- Do not commit `.env` to source control. Add `.env` to your `.gitignore`. +- The generated deployment YAML in `main.tf` is minimal. Update `main.tf` or replace the YAML generation if you need probes, autoscale, or advanced settings. +- This approach uses `az ml` CLI; for production, consider CI/CD pipelines that inject secrets securely or use ARM/Bicep templates. + +If you want, I can: +- Add `.env` to the repository `.gitignore` for you +- Expand the generated deployment YAML to include autoscale, probes, or scaling rules +- Convert this flow to an ARM/Bicep template or a GitHub Actions workflow + + +### Endpoint test with test_deploy.sh + +az extension update -n ml + +example + +az ml online-deployment get-logs -g KA-SAND-RG -w ml-sandbox-core -e whisper-edp-v1 -n whisper-fine-tuned-3 -l 100 + diff --git a/apps/whisper_fine_tuning/deployment/register/register_model.py b/apps/whisper_fine_tuning/deployment/register/register_model.py new file mode 100644 index 0000000..9a581cb --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/register/register_model.py @@ -0,0 +1,211 @@ +import os +import sys +import argparse +import logging +from datetime import datetime + +import mlflow +from mlflow.tracking import MlflowClient + +# Fix the path to point to the repository root where 'src' directory is located +repo_root = os.path.join(os.path.dirname(__file__), "..", "..") +sys.path.insert(0, repo_root) + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def get_latest_run_from_experiment(experiment_name: str) -> str: + """ + Get the latest run ID from the specified experiment. + + Args: + experiment_name: Name of the MLflow experiment + + Returns: + run_id: The ID of the latest run + """ + client = MlflowClient() + + try: + experiment = client.get_experiment_by_name(experiment_name) + if experiment is None: + raise ValueError(f"Experiment '{experiment_name}' not found") + + experiment_id = experiment.experiment_id + + # Get runs from experiment, sorted by start time (latest first) + runs = client.search_runs( + experiment_ids=[experiment_id], + order_by=["start_time DESC"], + max_results=1 + ) + + if not runs: + raise ValueError(f"No runs found in experiment '{experiment_name}'") + + latest_run = runs[0] + logger.info(f"Found latest run: {latest_run.info.run_id}") + logger.info(f"Run status: {latest_run.info.status}") + logger.info(f"Run start time: {datetime.fromtimestamp(latest_run.info.start_time / 1000)}") + + return latest_run.info.run_id + + except Exception as e: + logger.error(f"Error getting latest run: {e}") + raise + + +def register_model(run_id: str, model_name: str, artifact_path: str = "model") -> str: + """ + Register a model from the specified run. + + Args: + run_id: MLflow run ID containing the model + model_name: Name to register the model under + artifact_path: Path to the model artifact in the run + + Returns: + model_version: The version number of the registered model + """ + client = MlflowClient() + + try: + # Construct the model URI + model_uri = f"runs:/{run_id}/{artifact_path}" + + # Register the model + model_version = mlflow.register_model( + model_uri=model_uri, + name=model_name + ) + + logger.info(f"Model registered successfully!") + logger.info(f"Model name: {model_name}") + logger.info(f"Model version: {model_version.version}") + logger.info(f"Model URI: {model_uri}") + + return model_version.version + + except Exception as e: + logger.error(f"Error registering model: {e}") + raise + + +def set_model_stage(model_name: str, version: str, stage: str = "Staging"): + """ + Set the stage for a model version. + + Args: + model_name: Name of the registered model + version: Version number of the model + stage: Stage to set (None, Staging, Production, Archived) + """ + client = MlflowClient() + + try: + client.transition_model_version_stage( + name=model_name, + version=version, + stage=stage + ) + + logger.info(f"Model {model_name} version {version} moved to {stage} stage") + + except Exception as e: + logger.error(f"Error setting model stage: {e}") + raise + + +def main(): + parser = argparse.ArgumentParser( + description="Register the latest model from an MLflow experiment" + ) + + parser.add_argument( + "--experiment_name", + type=str, + required=True, + help="Name of the MLflow experiment to get the latest run from" + ) + + parser.add_argument( + "--model_name", + type=str, + default=None, + help="Name to register the model under. If not provided, will be auto-generated from experiment name" + ) + + parser.add_argument( + "--artifact_path", + type=str, + default="model", + help="Path to the model artifact in the run (default: 'model')" + ) + + parser.add_argument( + "--stage", + type=str, + default="Staging", + choices=["None", "Staging", "Production", "Archived"], + help="Stage to set for the registered model (default: 'Staging')" + ) + + parser.add_argument( + "--also_register_adapter", + action="store_true", + help="Also register the PEFT adapter if available" + ) + + args = parser.parse_args() + + # Generate model name if not provided + if args.model_name is None: + # Extract a clean model name from experiment name + # e.g., "whisper-gujarati-whisper-large-v2-lora-20251015" -> "whisper-gujarati-large-v2" + experiment_parts = args.experiment_name.split("-") + if len(experiment_parts) >= 4: + args.model_name = f"whisper-{experiment_parts[1]}-{experiment_parts[3]}-{experiment_parts[4]}" + else: + args.model_name = args.experiment_name.replace("-", "_") + + logger.info(f"Auto-generated model name: {args.model_name}") + + try: + # Get the latest run from the experiment + logger.info(f"Getting latest run from experiment: {args.experiment_name}") + run_id = get_latest_run_from_experiment(args.experiment_name) + + # Register the main model + logger.info(f"Registering model: {args.model_name}") + version = register_model(run_id, args.model_name, args.artifact_path) + + # Set the stage + if args.stage != "None": + logger.info(f"Setting model stage to: {args.stage}") + set_model_stage(args.model_name, version, args.stage) + + # Also register adapter if requested + if args.also_register_adapter: + try: + adapter_model_name = f"{args.model_name}-adapter" + logger.info(f"Registering adapter model: {adapter_model_name}") + adapter_version = register_model(run_id, adapter_model_name, "adapter_peft") + + if args.stage != "None": + set_model_stage(adapter_model_name, adapter_version, args.stage) + + except Exception as e: + logger.warning(f"Could not register adapter model: {e}") + + logger.info("Model registration completed successfully!") + logger.info(f"You can now use the model with: mlflow.transformers.load_model('models:/{args.model_name}/{version}')") + + except Exception as e: + logger.error(f"Model registration failed: {e}") + sys.exit(1) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/apps/whisper_fine_tuning/deployment/training/environment.yml b/apps/whisper_fine_tuning/deployment/training/environment.yml new file mode 100644 index 0000000..4fff3cc --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/training/environment.yml @@ -0,0 +1,29 @@ +name: whisper-fine-tuning +channels: + - conda-forge + - defaults +dependencies: + - python=3.10 + - pip + - numpy + - pandas + - scikit-learn + - pytorch + - torchaudio + - ffmpeg + - pip: + - ffmpeg-python + - transformers + - datasets + - peft + - mlflow + - accelerate + - bitsandbytes + - soundfile + - huggingface_hub + - evaluate + - gradio + - sentencepiece + - protobuf + - typing-extensions + - torchcodec diff --git a/apps/whisper_fine_tuning/deployment/training/training_job.yaml b/apps/whisper_fine_tuning/deployment/training/training_job.yaml new file mode 100644 index 0000000..fa22132 --- /dev/null +++ b/apps/whisper_fine_tuning/deployment/training/training_job.yaml @@ -0,0 +1,16 @@ +$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json +command: > + python deployment/job_train.py --dataset ./data/dataset_silver +code: ../src/core +environment: azureml:whisper-fine-tuning:1 +#compute: azureml:xxA100 +display_name: whisper-fine-tuning-job +description: Fine-tune Whisper model using job.py +experiment_name: whisper-fine-tuning-exp +resources: + instance_type: Standard_NC24ads_A100_v4 + instance_count: 1 +outputs: + output_dir: + mode: rw_mount + path: azureml://datastores/workspaceblobstore/paths/whisper-fine-tuning-output/§ 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"5cefac89" + }, + "source": [ + "# Whisper-large-V2 fine tuned simple usage " + ] + }, + { + "cell_type": "markdown", + "id": "625e47a0", + "metadata": { + "id": "625e47a0" + }, + "source": [ + "## 1. Environment Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1da5fff", + "metadata": { + "id": "e1da5fff" + }, + "outputs": [], + "source": [ + "import os\n", + "import torch\n", + "\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n", + "print(\"CUDA Available:\", torch.cuda.is_available())\n", + "if torch.cuda.is_available():\n", + " print(\"CUDA Version:\", torch.version.cuda)\n", + " print(\"Number of CUDA Devices:\", torch.cuda.device_count())\n", + " current_device = torch.cuda.current_device()\n", + " print(\"Current CUDA Device:\", current_device)\n", + " print(\"CUDA Device Name:\", torch.cuda.get_device_name(current_device))\n", + "else:\n", + " print(\"CUDA is not available. Please check your GPU configuration.\")\n", + "\n", + "import sys\n", + "sys.path.append(os.path.dirname(os.getcwd())) # Adds the parent directory of the current working directory to sys.path\n", + "print(\"Current working directory added to sys.path:\", os.getcwd())\n", + "\n", + "## 2. Load Model from MLflow (pyfunc and transformers flavors)\n", + "import mlflow\n", + "\n", + "from transformers import (\n", + " AutomaticSpeechRecognitionPipeline,\n", + " WhisperForConditionalGeneration,\n", + " WhisperTokenizer,\n", + " WhisperProcessor,\n", + ")\n", + "\n", + "from peft import PeftModel, PeftConfig\n", + "\n", + "import warnings\n", + "\n", + "# Suppress all warnings\n", + "warnings.filterwarnings(\"ignore\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5b47e2c", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from dotenv import load_dotenv\n", + "# Load environment variables from a .env file if it exists\n", + "load_dotenv()\n", + "# Retrieve the model URI from the virtual environment\n", + "model_uri = os.environ.get(\"model_uri\", \"models:/whisper-Mayoruna-v3/1\")\n", + "mlflow_uri = os.environ.get(\"mlflow_uri\", None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c784d36b", + "metadata": {}, + "outputs": [], + "source": [ + "local_folder = \"../model/\"\n", + "artifacts_dir = mlflow.artifacts.download_artifacts(model_uri, dst_path=local_folder)\n", + "print(f\"Artifacts downloaded to: {artifacts_dir}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef643524", + "metadata": {}, + "outputs": [], + "source": [ + "# Try loading as Hugging Face pipeline (recommended for Whisper)\n", + "try:\n", + " pipe = mlflow.transformers.load_model(model_uri)\n", + " print(\"Loaded MLflow model as Hugging Face pipeline.\")\n", + "except Exception as e:\n", + " print(\"Could not load as pipeline:\", e)\n", + " pipe = None\n" + ] + }, + { + "cell_type": "markdown", + "id": "0f0928f0", + "metadata": {}, + "source": [ + "## 2. Fallback: load as pyfunc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fec43666", + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " loaded_model = mlflow.pyfunc.load_model(model_uri)\n", + " print(\"Loaded MLflow model as pyfunc.\")\n", + "except Exception as e:\n", + " print(\"Could not load as pyfunc:\", e)\n", + " loaded_model = None" + ] + }, + { + "cell_type": "markdown", + "id": "9745bfe1", + "metadata": {}, + "source": [ + "## 3. Inference Example (Audio Transcription)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49f19801", + "metadata": {}, + "outputs": [], + "source": [ + "audio_path = \"../src/core/create_data/mayoruna/train_data/audio20.opus\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb7104b3", + "metadata": {}, + "outputs": [], + "source": [ + "generate_kwargs={\n", + " \"repetition_penalty\": 1.2,\n", + " \"no_repeat_ngram_size\": 3,\n", + " \"max_new_tokens\": 128,\n", + " \"do_sample\": False,\n", + " }\n", + "\n", + "\n", + "if pipe is not None:\n", + " # Hugging Face pipeline usage\n", + " result = pipe(audio_path, generate_kwargs=generate_kwargs)\n", + " print(\"Transcription:\", result[\"text\"])\n", + "elif loaded_model is not None:\n", + " # MLflow pyfunc ussage (expects a DataFrame)\n", + " import pandas as pd\n", + " df = pd.DataFrame({\"inputs\": [audio_path]})\n", + " result = loaded_model.predict(df)\n", + " print(\"Transcription:\", result)\n", + "else:\n", + " print(\"No model loaded for inference.\")" + ] + }, + { + "cell_type": "markdown", + "id": "b07e7546", + "metadata": {}, + "source": [ + "WER considers three types of errors:\n", + "Substitutions – wrong word used instead of the correct one\n", + "Insertions – extra words added\n", + "Deletions – missing words" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec06cc2f", + "metadata": {}, + "outputs": [], + "source": [ + "from jiwer import wer\n", + "\n", + "# Load ground truth from the file\n", + "ground_truth_file = \"../src/core/create_data/text\"\n", + "with open(ground_truth_file, \"r\") as file:\n", + " ground_truth_data = file.readlines()\n", + "\n", + "# Parse ground truth into a dictionary\n", + "ground_truth_dict = {}\n", + "for line in ground_truth_data:\n", + " audio_id, text = line.split(maxsplit=1)\n", + " ground_truth_dict[audio_id] = text.strip()\n", + "\n", + "# Get the transcription result\n", + "transcription_result = result[\"text\"]\n", + "\n", + "# Extract the ground truth for the corresponding audio file\n", + "audio_id = audio_path.split(\"/\")[-1].split(\".\")[0] # Extract audio ID from the file path\n", + "ground_truth_text = ground_truth_dict.get(audio_id, \"\")\n", + "\n", + "# Calculate WER\n", + "error_rate = wer(ground_truth_text, transcription_result)\n", + "print(f\"Transcription: {transcription_result}\")\n", + "print(f\"Ground Truth: {ground_truth_text}\")\n", + "print(f\"Word Error Rate (WER): {error_rate}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "f364433b", + "metadata": {}, + "source": [ + "## 4. Load Model from Local Checkpoint (PEFT)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8583ce3", + "metadata": {}, + "outputs": [], + "source": [ + "audio_path = \"../src/core/create_data/evaluation_data/audio3.opus\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1005a3c", + "metadata": {}, + "outputs": [], + "source": [ + "local_model_path = \"../model/model\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fdb34e0", + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " peft_config = PeftConfig.from_pretrained(local_model_path)\n", + " model = WhisperForConditionalGeneration.from_pretrained(\n", + " peft_config.base_model_name_or_path, load_in_8bit=True, device_map=\"auto\"\n", + " )\n", + " model = PeftModel.from_pretrained(model, local_model_path)\n", + " tokenizer = WhisperTokenizer.from_pretrained(peft_config.base_model_name_or_path)\n", + " processor = WhisperProcessor.from_pretrained(peft_config.base_model_name_or_path)\n", + " print(f\"Loaded local PEFT model - {peft_config.base_model_name_or_path}\")\n", + "except Exception as e:\n", + " print(\"Could not load local PEFT model:\", e)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45e0c877", + "metadata": {}, + "outputs": [], + "source": [ + "feature_extractor = processor.feature_extractor\n", + "forced_decoder_ids = processor.get_decoder_prompt_ids(task=\"transcribe\")\n", + "pipe = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)\n", + "\n", + "generate_kwargs={\n", + " \"repetition_penalty\": 1.2,\n", + " \"no_repeat_ngram_size\": 3,\n", + " \"max_new_tokens\": 128,\n", + " \"do_sample\": False,\n", + " \"forced_decoder_ids\": forced_decoder_ids\n", + " }\n", + "\n", + "def transcribe(audio):\n", + " with torch.cuda.amp.autocast():\n", + " text = pipe(audio, generate_kwargs=generate_kwargs)[\"text\"]\n", + " return text\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a587ce13", + "metadata": {}, + "outputs": [], + "source": [ + "audio_path = \"../src/core/create_data/evaluation_data/audio1.opus\"\n", + "transcription_result = transcribe(audio_path)\n", + "\n", + "# Extract the ground truth for the corresponding audio file\n", + "audio_id = audio_path.split(\"/\")[-1].split(\".\")[0] # Extract audio ID from the file path\n", + "ground_truth_text = ground_truth_dict.get(audio_id, \"\")\n", + "print(\"Transcription Result:\", transcription_result)\n", + "print(f\"Ground Truth: {ground_truth_text}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "afd53329", + "metadata": {}, + "outputs": [], + "source": [ + "### Inference with nvidia/parakeet-rnnt-110m-da-dk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f0505dd", + "metadata": {}, + "outputs": [], + "source": [ + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a17f4b18", + "metadata": {}, + "outputs": [], + "source": [ + "from nemo.collections.asr.models import EncDecRNNTModel\n", + "\n", + "# Path to the .nemo model\n", + "nemo_model_path = \"../nemo_rnnt_da/parakeet-rnnt-110m-da-dk-finetuned.nemo\"\n", + "\n", + "# Load the NeMo model\n", + "try:\n", + " nemo_model = EncDecRNNTModel.restore_from(nemo_model_path)\n", + " print(\"NeMo model loaded successfully.\")\n", + "except Exception as e:\n", + " print(\"Failed to load NeMo model:\", e)\n", + "\n", + "# Perform inference\n", + "def transcribe_audio(audio_file):\n", + " try:\n", + " transcription = nemo_model.transcribe([audio_file])\n", + " return transcription[0]\n", + " except Exception as e:\n", + " print(\"Error during transcription:\", e)\n", + " return None\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7566c6a", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.stdout.reconfigure(encoding=\"utf-8\") # run once, early in the notebook\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "295d417c", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Example usage\n", + "audio_path = \"../src/core/create_data/matis/train_data/audio1.ogg\"\n", + "transcription_result = transcribe_audio(audio_path)\n", + "print(\"Transcription Result:\", transcription_result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ced1536d", + "metadata": {}, + "outputs": [], + "source": [ + "⁇ in b ⁇ dawaki abrimenkin n ⁇ ndoxita ⁇\n", + "\n", + "Mibi min benefício awestenda ikek isnukek?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1009713c", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# Perform a simple inference example\n", + "test_audio_path = \"../src/core/create_data/test_data/audio9.opus\"\n", + "\n", + "if pipe is not None:\n", + " # Hugging Face pipeline usage\n", + " result = pipe(test_audio_path)\n", + " print(\"Transcription:\", result[\"text\"])\n", + "elif loaded_model is not None:\n", + " # MLflow pyfunc usage (expects a DataFrame)\n", + " df = pd.DataFrame({\"inputs\": [test_audio_path]})\n", + " result = loaded_model.predict(df)\n", + " print(\"Transcription:\", result)\n", + "else:\n", + " print(\"No model loaded for inference.\")" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "machine_shape": "hm", + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "whisper-ft-py3.12", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + }, + "widgets": { + 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["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +enabler = ["pytest-enabler (>=2.2)"] +test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] + +[metadata] +lock-version = "2.1" +python-versions = ">=3.11,<4.0" +content-hash = "f4ac7940883bdf3fab365a634e5be933f96a2327acbd4340fe194bceafeb7c06" diff --git a/apps/whisper_fine_tuning/pyproject.toml b/apps/whisper_fine_tuning/pyproject.toml new file mode 100644 index 0000000..2ae5a24 --- /dev/null +++ b/apps/whisper_fine_tuning/pyproject.toml @@ -0,0 +1,48 @@ +[tool.poetry] +name = "whisper-ft" +version = "0.1.0" +description = "" +authors = ["Karina Assini "] + +[tool.poetry.dependencies] +python = ">=3.11,<4.0" +pyarrow="16.1.0" +ipywidgets = "*" +torch = "*" # note: prefer installing via conda for correct wheels +torchcodec = "*" +accelerate = "*" +notebook = "*" 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a/apps/whisper_fine_tuning/src/core/data_prep/README.md b/apps/whisper_fine_tuning/src/core/data_prep/README.md new file mode 100644 index 0000000..306b82e --- /dev/null +++ b/apps/whisper_fine_tuning/src/core/data_prep/README.md @@ -0,0 +1,218 @@ +# Create Data Feature + +This module provides data preparation functionality for Whisper fine-tuning, converting raw audio files and transcriptions into a format suitable for training, testing, and evaluation. + +## Overview + +The `data_prep` feature is designed to prepare audio datasets for Whisper model fine-tuning by: +- Processing audio files and their corresponding transcriptions +- Organizing data into training, testing, and evaluation sets +- Converting data into HuggingFace datasets format +- Handling audio resampling to 16kHz (Whisper's expected format) + +## Directory Structure + +The data preparation entry point remains `src/core/create_data/data_prep.py`. The raw speech corpora now live in `data/raw/audios/`, where each language pack contains the artifacts required by the data preparation script: + +``` +data/raw/audios// +├── audio_paths # File mapping audio IDs to file paths +├── text # File containing transcriptions +├── train_data/ # Training audio files (.opus format) +├── test_data/ # Testing audio files (.opus format) +└── evaluation_data/ # Evaluation audio files (.opus format) +``` + +Examples include `data/raw/audios/matis` and `data/raw/audios/mayoruna`. + +## File Formats + +### audio_paths +Maps audio IDs to their corresponding file paths within the selected language pack: +``` +audio1 data/raw/audios/matis/evaluation_data/audio1.opus +audio2 data/raw/audios/matis/evaluation_data/audio2.opus +audio6 data/raw/audios/matis/train_data/audio6.opus +... +``` + +### text +Contains transcriptions paired with audio IDs: +``` +audio1 min cartão bëdambo iquec +audio2 min cpf dada ua +audio6 ada min conta iste bune? +... +``` + +## Usage + +### Data Preparation Pipeline + +Run the data preparation pipeline from the project root to create domain-specific datasets. Point `--source_data_dir` to the language pack in `data/raw/audios` that you want to process. + +**Training Data:** +```bash +python src/core/data_prep/data_prep.py \ + --source_data_dir data/raw/audios/matis \ + --output_data_dir data/raw/samples/training \ + --domain train +``` + +**Testing Data:** +```bash +python src/core/data_prep/data_prep.py \ + --source_data_dir data/raw/audios/matis \ + --output_data_dir data/raw/samples/testing \ + --domain test +``` + +**Evaluation Data:** +```bash +python src/core/data_prep/data_prep.py \ + --source_data_dir data/raw/audios/matis \ + --output_data_dir data/raw/samples/evaluation \ + --domain evaluation +``` + + +### Integration with Training Pipeline + +After preparing your datasets, run the complete pipeline locally: + +```bash +python src/core/data_prep/main_silver_data_prep.py --train_datasets data/raw/samples/training --eval_datasets data/raw/samples/evaluation --test_datasets data/raw/samples/testing +``` + +This command will use the prepared datasets for the complete Whisper fine-tuning workflow. + +### Command Line Arguments + +- `--source_data_dir` (required): Path to the directory containing `audio_paths` and `text` files +- `--output_data_dir` (optional): Output directory path (default: "op_data_dir") +- `--domain` (optional): Specify which domain to process - choices: ["train", "test", "evaluation", "all"] (default: "all") + +## Data Processing Pipeline + +1. **File Validation**: Checks for existence of `audio_paths` and `text` files +2. **Data Loading**: Reads audio paths and transcriptions from input files +3. **Domain Filtering**: Optionally filters data by domain (train/test/evaluation) +4. **Data Matching**: Matches audio files with their transcriptions using audio IDs +5. **Dataset Creation**: Creates HuggingFace dataset with: + - Audio column: Audio files resampled to 16kHz + - Sentence column: Corresponding transcriptions +6. **Output**: Saves processed dataset to disk in HuggingFace format + +## Data Validation + +The script performs several validation steps: +- Ensures input files exist +- Validates audio_paths file format +- Matches audio IDs between `audio_paths` and `text` files +- Reports statistics on matched and skipped entries +- Provides sample output for verification + +## Output Format + +The processed dataset is saved as a HuggingFace Dataset with: +- **audio**: Audio arrays resampled to 16kHz sampling rate +- **sentence**: String transcriptions + +## Example Output + +When running the training data preparation: +```bash +$ python src/core/create_data/data_prep.py --source_data_dir data/raw/audios/matis --output_data_dir data/raw/training --domain train +``` + +Expected output: +``` +INFO:__main__:Total audio_paths lines: 57, total text lines: 57 +INFO:__main__:After domain filter 'train': audio entries = 49 +INFO:__main__:Matched transcript entries: 49; skipped (no audio): 0 +INFO:__main__:Data preparation done. Saved to data/raw/training +INFO:__main__:Sample data: +Dataset({ + features: ['audio', 'sentence'], + num_rows: 5 +}) +``` + +**Current Dataset Statistics:** +- **Training**: 49 audio files (audio1-audio30, audio32-audio55) +- **Testing**: 3 audio files (audio31, audio42, audio57) +- **Evaluation**: 5 audio files (audio1-audio5) +- **Total**: 57 audio files + +## Audio Format Support + +- Supports `.opus` audio files +- Automatically resamples audio to 16kHz for Whisper compatibility +- Preserves audio quality during processing + +## Error Handling + +The script handles common issues gracefully: +- Missing input files +- Malformed lines in `audio_paths` +- Unmatched audio IDs +- Empty datasets after filtering + +## Integration with Whisper Fine-tuning + +This prepared dataset integrates seamlessly with the Whisper fine-tuning pipeline: + +1. **Data Preparation**: Use the commands above to create domain-specific datasets +2. **Pipeline Integration**: The prepared datasets are automatically consumed by `deployment/job_data.py` +3. **Training Flow**: The datasets feed into the training configuration and model fine-tuning process + +### Typical Workflow: +```bash +# Step 1: Prepare training data +python src/core/create_data/data_prep.py \ + --source_data_dir data/raw/audios/matis \ + --output_data_dir data/raw/training \ + --domain train + +# Step 2: Prepare evaluation data +python src/core/create_data/data_prep.py \ + --source_data_dir data/raw/audios/matis \ + --output_data_dir data/raw/evaluation \ + --domain evaluation + +# Step 3: Prepare testing data +python src/core/create_data/data_prep.py \ + --source_data_dir data/raw/audios/matis \ + --output_data_dir data/raw/testing \ + --domain test + +# Step 4: Run the complete pipeline +python deployment/training/job_data.py --train_datasets data/raw/training --eval_datasets data/raw/evaluation --test_datasets data/raw/testing +``` + +## Sample Data + +The current dataset contains audio samples in what appears to be a indigenous or regional language, with transcriptions that may be used for: +- Speech recognition model training +- Language preservation efforts +- Multi-lingual ASR development + +## Requirements + +- Python 3.8+ +- HuggingFace `datasets` library +- Audio processing capabilities for `.opus` files + +## Troubleshooting + +**No matching audio+text pairs found:** +- Verify audio IDs in `text` file match those in `audio_paths` +- Check domain selection matches available data folders + +**Missing input files:** +- Ensure both `audio_paths` and `text` files exist in source directory +- Verify file permissions and accessibility + +**Audio loading issues:** +- Confirm audio files exist at specified paths +- Check audio file format compatibility \ No newline at end of file diff --git a/apps/whisper_fine_tuning/src/core/data_prep/load_data.py b/apps/whisper_fine_tuning/src/core/data_prep/load_data.py new file mode 100644 index 0000000..30f5b72 --- /dev/null +++ b/apps/whisper_fine_tuning/src/core/data_prep/load_data.py @@ -0,0 +1,134 @@ +from dataclasses import dataclass +from typing import Any, Dict, List, Union + +import torch +from datasets import Audio, DatasetDict, concatenate_datasets, load_from_disk +from transformers.models.whisper.english_normalizer import BasicTextNormalizer + + +class DataPreparer: + """ + A class that encapsulates dataset loading, preparation, and filtering. + """ + + def __init__( + self, + args, + processor, + normalizer: BasicTextNormalizer, + do_lower_case: bool = False, + do_remove_punctuation: bool = False, + ): + self.args = args + self.processor = processor + self.normalizer = normalizer + self.do_lower_case = do_lower_case + self.do_remove_punctuation = do_remove_punctuation + self.max_label_length = 100 # will be set later from model config if necessary + self.min_input_length = 0.0 + self.max_input_length = 30.0 + + def set_max_label_length(self, max_length: int): + """Set maximum label length from model configuration.""" + self.max_label_length = max_length + + def load_custom_dataset(self, split: str) -> Any: + """Load and prepare a concatenated dataset from disk for a given split.""" + datasets = [] + if split == "train": + for dset in self.args.train_datasets: + datasets.append(load_from_disk(dset)) + elif split == "eval": + for dset in self.args.eval_datasets: + datasets.append(load_from_disk(dset)) + elif split == "test": + for dset in self.args.test_datasets: + datasets.append(load_from_disk(dset)) + else: + raise ValueError("Split must be either 'train' or 'eval'.") + ds = concatenate_datasets(datasets) + return ds.shuffle(seed=22) + + def prepare_dataset(self, batch: Dict) -> Dict: + """ + Process a single example: resample audio, extract features, normalize + transcription text, and encode the transcription. + """ + audio = batch["audio"] + # Extract log-Mel input features from audio array + batch["input_features"] = self.processor.feature_extractor( + audio["array"], sampling_rate=audio["sampling_rate"] + ).input_features[0] + batch["input_length"] = len(audio["array"]) / audio["sampling_rate"] + + transcription = batch["sentence"] + if self.do_lower_case: + transcription = transcription.lower() + if self.do_remove_punctuation: + transcription = self.normalizer(transcription).strip() + + batch["labels"] = self.processor.tokenizer(transcription).input_ids + return batch + + def is_in_length_range(self, length, labels): + return ( + self.min_input_length < length < self.max_input_length + and 0 < len(labels) < self.max_label_length + ) + + def prepare(self) -> DatasetDict: + """Load, cast, process and filter datasets.""" + dataset = DatasetDict() + dataset["train"] = self.load_custom_dataset("train") + dataset["eval"] = self.load_custom_dataset("eval") + dataset["test"] = self.load_custom_dataset("test") + + print("Dataset loaded so far:") + print(dataset) + + dataset = dataset.cast_column( + "audio", Audio(sampling_rate=self.args.sampling_rate) + ) + dataset = dataset.map(self.prepare_dataset, num_proc=self.args.num_proc) + dataset = dataset.filter( + self.is_in_length_range, + input_columns=["input_length", "labels"], + num_proc=self.args.num_proc, + ) + return dataset + + +@dataclass +class DataCollatorSpeechSeq2SeqWithPadding: + processor: Any + + def __call__( + self, features: List[Dict[str, Union[List[int], torch.Tensor]]] + ) -> Dict[str, torch.Tensor]: + # split inputs and labels since they have to be of different lengths and need different padding methods + # first treat the audio inputs by simply returning torch tensors + input_features = [ + {"input_features": feature["input_features"]} for feature in features + ] + batch = self.processor.feature_extractor.pad( + input_features, return_tensors="pt" + ) + + # get the tokenized label sequences + label_features = [{"input_ids": feature["labels"]} for feature in features] + # pad the labels to max length + labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt") + + # replace padding with -100 to ignore loss correctly + labels = labels_batch["input_ids"].masked_fill( + labels_batch.attention_mask.ne(1), -100 + ) + + # if bos token is appended in previous tokenization step, + # cut bos token here as it's append later anyways + if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item(): + labels = labels[:, 1:] + + batch["labels"] = labels + + return batch diff --git a/apps/whisper_fine_tuning/src/core/data_prep/main_data_prep.py b/apps/whisper_fine_tuning/src/core/data_prep/main_data_prep.py new file mode 100644 index 0000000..612e89f --- /dev/null +++ b/apps/whisper_fine_tuning/src/core/data_prep/main_data_prep.py @@ -0,0 +1,172 @@ +import argparse +import logging +from pathlib import Path + +from datasets import Audio, Dataset, Value + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def main(): + parser = argparse.ArgumentParser( + description="Preliminary data preparation script before Whisper Fine-tuning." + ) + parser.add_argument( + "--source_data_dir", + type=str, + required=True, + help="Path to the directory containing the audio_paths and text files.", + ) + parser.add_argument( + "--output_data_dir", + type=str, + required=False, + default="op_data_dir", + help="Output data directory path.", + ) + parser.add_argument( + "--domain", + type=str, + required=False, + default="all", + choices=["train", "test", "evaluation", "all"], + help="Specify which domain to process (train, test, evaluation, all). Default is all.", + ) + + args = parser.parse_args() + + src_dir = Path(args.source_data_dir).resolve() + + repo_root = None + for candidate in [src_dir] + list(src_dir.parents): + if (candidate / "pyproject.toml").exists() or (candidate / ".git").exists(): + repo_root = candidate + break + if repo_root is None: + repo_root = src_dir + + audio_paths_file = src_dir / "audio_paths" + text_file = src_dir / "text" + + if not audio_paths_file.exists() or not text_file.exists(): + logger.error( + "Missing input files: ensure 'audio_paths' and 'text' exist in %s", src_dir + ) + return + + with open(audio_paths_file, "r", encoding="utf-8") as scp_file: + scp_lines = [line.strip() for line in scp_file if line.strip()] + + with open(text_file, "r", encoding="utf-8") as txt_file: + txt_lines = [line.strip() for line in txt_file if line.strip()] + + logger.info( + "Total audio_paths lines: %d, total text lines: %d", + len(scp_lines), + len(txt_lines), + ) + + scp_entries = [] + for line in scp_lines: + tokens = line.split() + if len(tokens) < 2: + logger.warning("Skipping malformed audio_paths line: %s", line) + continue + audio_id = tokens[0] + path = tokens[1] + scp_entries.append((audio_id, path)) + + domain = args.domain.lower() + if domain != "all": + domain_folder = f"{domain}_data" + scp_entries = [(aid, p) for (aid, p) in scp_entries if domain_folder in p] + logger.info( + "After domain filter '%s': audio entries = %d", domain, len(scp_entries) + ) + if not scp_entries: + logger.error("No audio entries found for domain: %s", domain) + return + + scp_map = {} + for aid, path in scp_entries: + if aid not in scp_map: + scp_map[aid] = path + + raw_audio_paths = [] + sentences = [] + skipped_txt = 0 + for line in txt_lines: + tokens = line.split() + if not tokens: + continue + audio_id = tokens[0] + sentence = " ".join(tokens[1:]).strip() + if audio_id in scp_map: + raw_audio_paths.append(scp_map[audio_id]) + sentences.append(sentence) + else: + skipped_txt += 1 + + logger.info( + "Matched transcript entries: %d; skipped (no audio): %d", + len(sentences), + skipped_txt, + ) + + if not raw_audio_paths: + logger.error( + "No matching audio+text pairs found. Check that audio IDs in 'text' match those in 'audio_paths' and domain selection." + ) + return + + resolved_audio_paths = [] + for raw_path in raw_audio_paths: + candidates = [] + + candidate = Path(raw_path).expanduser() + candidates.append(candidate) + + if not candidate.is_absolute(): + candidates.append((src_dir / raw_path).expanduser()) + candidates.append((repo_root / raw_path).expanduser()) + + resolved = None + for cand in candidates: + cand_resolved = cand.resolve() + if cand_resolved.exists(): + resolved = cand_resolved + break + + if resolved is None: + logger.error("Could not resolve audio file: %s", raw_path) + return + + resolved_audio_paths.append(str(resolved)) + + audio_dataset = Dataset.from_dict( + { + "audio": resolved_audio_paths, + "audio_path": resolved_audio_paths, + "sentence": sentences, + } + ) + audio_dataset = audio_dataset.cast_column( + "audio", Audio(sampling_rate=16_000, decode=False) + ) + audio_dataset = audio_dataset.cast_column("audio_path", Value("string")) + audio_dataset = audio_dataset.cast_column("sentence", Value("string")) + audio_dataset.save_to_disk(args.output_data_dir) + logger.info("Data preparation done. Saved to %s", args.output_data_dir) + + sample = audio_dataset.select(range(min(5, len(audio_dataset)))) + logger.info("Sample data:\n%s", sample) + first_row = audio_dataset[0] + logger.info("First row data:\n%s", first_row) + logger.info("Printing all sentences in the dataset:") + for sentence in audio_dataset["sentence"]: + logger.info(sentence) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/apps/whisper_fine_tuning/src/core/data_prep/main_silver_data_prep.py b/apps/whisper_fine_tuning/src/core/data_prep/main_silver_data_prep.py new file mode 100644 index 0000000..5453160 --- /dev/null +++ b/apps/whisper_fine_tuning/src/core/data_prep/main_silver_data_prep.py @@ -0,0 +1,168 @@ +import os +import sys + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) +import argparse +import logging + +import torch +from transformers import WhisperForConditionalGeneration, WhisperProcessor +from transformers.models.whisper.english_normalizer import BasicTextNormalizer + +# Fix the path to point to the repository root where 'src' directory is located +repo_root = os.path.join(os.path.dirname(__file__), "..", "..") +sys.path.insert(0, repo_root) + +from load_data import DataPreparer + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def main(): + parser = argparse.ArgumentParser( + description="Fine-tuning script for Whisper Models of various sizes." + ) + parser.add_argument( + "--model_name", + type=str, + default="openai/whisper-small", + help="Huggingface model name to fine-tune. Eg: openai/whisper-small", + ) + + parser.add_argument( + "--apply_lora", + type=bool, + default=True, + ) + + parser.add_argument( + "--language", + type=str, + default="", + help="Language the model is being adapted to in Camel case.", + ) + parser.add_argument( + "--sampling_rate", type=int, default=16000, help="Sampling rate of audios." + ) + parser.add_argument( + "--num_proc", + type=int, + default=2, + help="Number of parallel jobs to run. Helps parallelize the dataset prep stage.", + ) + parser.add_argument( + "--train_strategy", + type=str, + default="steps", + help="Training strategy. Choose between steps and epoch.", + ) + + parser.add_argument( + "--resume_from_ckpt", + type=str, + default=None, + help="Path to a trained checkpoint to resume training from.", + ) + parser.add_argument( + "--output_dir", + type=str, + default="output_model_dir", + help="Output directory for the checkpoints generated.", + ) + parser.add_argument( + "--train_datasets", + type=str, + nargs="+", + required=True, + help="List of datasets to be used for training.", + ) + parser.add_argument( + "--eval_datasets", + type=str, + nargs="+", + required=False, + help="List of datasets to be used for evaluation.", + ) + + parser.add_argument( + "--test_datasets", + type=str, + nargs="+", + required=False, + help="List of datasets to be used for testing.", + ) + args = parser.parse_args() + + if args.train_strategy not in ["steps", "epoch"]: + raise ValueError('The train strategy should be either "steps" or "epoch".') + + # Log arguments for transparency + logger.info("\n\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n\n") + logger.info("ARGUMENTS OF INTEREST:") + logger.info(vars(args)) + logger.info("\n\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n\n") + + gradient_checkpointing = True + freeze_feature_encoder = False + freeze_encoder = False + + do_lower_case = False + do_remove_punctuation = False + normalizer = BasicTextNormalizer() + + gpu_available = torch.cuda.is_available() + torch.cuda.device(0) + logger.info(f"CUDA Available: {gpu_available}") + if gpu_available: + logger.info(f"CUDA Version: {torch.version.cuda}") + logger.info(f"Number of CUDA Devices: {torch.cuda.device_count()}") + current_device = torch.cuda.current_device() + logger.info(f"Current CUDA Device: {current_device}") + logger.info(f"CUDA Device Name: {torch.cuda.get_device_name(current_device)}") + else: + logger.error("CUDA is not available. Please check your GPU configuration.") + + # Load models and processors. + processor = WhisperProcessor.from_pretrained(args.model_name, task="transcribe") + + logger.info("LOADING MODEL...") + model = WhisperForConditionalGeneration.from_pretrained(args.model_name) + + if model.config.decoder_start_token_id is None: + raise ValueError( + "Ensure that `config.decoder_start_token_id` is correctly defined" + ) + + if freeze_feature_encoder: + model.freeze_feature_encoder() + + if freeze_encoder: + model.freeze_encoder() + model.model.encoder.gradient_checkpointing = False + + if gradient_checkpointing: + model.config.use_cache = False + + model.config.forced_decoder_ids = None + model.config.suppress_tokens = [] + + # Initialize DataPreparer and set max label length from model + data_prep = DataPreparer( + args, processor, normalizer, do_lower_case, do_remove_punctuation + ) + data_prep.set_max_label_length(model.config.max_length) + + logger.info("DATASET PREPARATION IN PROGRESS...") + raw_dataset = data_prep.prepare() + + save_path = "./data/silver/dataset" + raw_dataset.save_to_disk(save_path) + logger.info(f"Dataset saved locally at {save_path}.") + + logger.info("DATASET PREPARATION COMPLETED") + + +if __name__ == "__main__": + main() diff --git a/apps/whisper_fine_tuning/src/core/evaluation/evaluation_process.py b/apps/whisper_fine_tuning/src/core/evaluation/evaluation_process.py new file mode 100644 index 0000000..b3b0787 --- /dev/null +++ b/apps/whisper_fine_tuning/src/core/evaluation/evaluation_process.py @@ -0,0 +1,248 @@ +import argparse +import logging +import os + +import evaluate +from datasets import Audio, load_from_disk +from tqdm import tqdm +from transformers import pipeline +from transformers.models.whisper.english_normalizer import BasicTextNormalizer + +logging.basicConfig(level=logging.INFO) + +wer_metric = evaluate.load("wer") +cer_metric = evaluate.load("cer") + + +def is_target_text_in_range(ref): + if ref.strip() == "ignore time segment in scoring": + return False + else: + return ref.strip() != "" + + +def get_text(sample): + if "text" in sample: + return sample["text"] + elif "sentence" in sample: + return sample["sentence"] + elif "normalized_text" in sample: + return sample["normalized_text"] + elif "transcript" in sample: + return sample["transcript"] + elif "transcription" in sample: + return sample["transcription"] + else: + raise ValueError( + "Expected transcript column of either 'text', 'sentence', 'normalized_text' or 'transcript'. Got sample of " + ".join{sample.keys()}. Ensure a text column name is present in the dataset." + ) + + +def get_text_column_names(column_names): + if "text" in column_names: + return "text" + elif "sentence" in column_names: + return "sentence" + elif "normalized_text" in column_names: + return "normalized_text" + elif "transcript" in column_names: + return "transcript" + elif "transcription" in column_names: + return "transcription" + + +whisper_norm = BasicTextNormalizer() + + +def normalise(batch): + batch["norm_text"] = whisper_norm(get_text(batch)) + return batch + + +def data(dataset): + for i, item in enumerate(dataset): + yield { + "array": item["audio"]["array"], + "sampling_rate": item["audio"]["sampling_rate"], + "reference": get_text(item), + "norm_reference": item["norm_text"], + } + + +def main(args): + if args.is_public_repo is False: + logging.info("Loading custom model from local checkpoint...") + + if not os.path.exists(args.ckpt_dir): + raise FileNotFoundError( + f"Checkpoint directory '{args.ckpt_dir}' does not exist." + ) + + else: + logging.info(f"Checkpoint directory '{args.ckpt_dir}' found.") + model_id = args.ckpt_dir + logging.info("Custom model and tokenizer loaded successfully.") + else: + logging.info("Loading HF model..") + model_id = args.hf_model + logging.info("Custom model and tokenizer loaded successfully.") + + whisper_asr = pipeline( + "automatic-speech-recognition", model=model_id, device=args.device, + generate_kwargs={ + "repetition_penalty": 1.2, + "no_repeat_ngram_size": 3, + "max_new_tokens": 128, + "do_sample": False + } + ) + + whisper_asr.model.config.forced_decoder_ids = ( + whisper_asr.tokenizer.get_decoder_prompt_ids( + language=args.language, task="transcribe" + ) + ) + + os.system(f"mkdir {args.output_dir}") + for dset in args.eval_datasets: + logging.info("Infering on the dataset: %s", dset) + dataset = load_from_disk(dset) + text_column_name = get_text_column_names(dataset.column_names) + dataset = dataset.cast_column("audio", Audio(sampling_rate=16000)) + dataset = dataset.map(normalise, num_proc=2) + dataset = dataset.filter( + is_target_text_in_range, input_columns=[text_column_name], num_proc=2 + ) + + # Log a sample from the dataset for inspection + sample_entry = dataset[0] + logging.info("Dataset sample:") + logging.info(sample_entry) + + predictions = [] + references = [] + norm_predictions = [] + norm_references = [] + + for out in tqdm( + whisper_asr(data(dataset), batch_size=args.batch_size), + desc="Decode Progress", + ): + predictions.append(out["text"]) + references.append(out["reference"][0]) + norm_predictions.append(whisper_norm(out["text"])) + norm_references.append(out["norm_reference"][0]) + + wer = wer_metric.compute(references=references, predictions=predictions) + wer = round(100 * wer, 2) + cer = cer_metric.compute(references=references, predictions=predictions) + cer = round(100 * cer, 2) + norm_wer = wer_metric.compute( + references=norm_references, predictions=norm_predictions + ) + norm_wer = round(100 * norm_wer, 2) + norm_cer = cer_metric.compute( + references=norm_references, predictions=norm_predictions + ) + norm_cer = round(100 * norm_cer, 2) + + logging.info("WER: %s", wer) + logging.info("CER: %s", cer) + logging.info("NORMALIZED WER: %s", norm_wer) + logging.info("NORMALIZED CER: %s", norm_cer) + + dset = dset.replace("/", "_") + op_file = args.output_dir + "/" + dset + if args.is_public_repo: + op_file = op_file + "_" + args.hf_model.replace("/", "_") + else: + op_file = op_file + "_" + args.ckpt_dir.split("/")[-1].replace("/", "_") + result_file = open(op_file, "w") + result_file.write("\nWER: " + str(wer) + "\n") + result_file.write("CER: " + str(cer) + "\n") + result_file.write("\nNORMALIZED WER: " + str(norm_wer) + "\n") + result_file.write("NORMALIZED CER: " + str(norm_cer) + "\n\n\n") + + for ref, hyp in zip(references, predictions): + result_file.write("REF: " + ref + "\n") + result_file.write("HYP: " + hyp + "\n") + result_file.write( + "------------------------------------------------------" + "\n" + ) + result_file.close() + + if args.is_public_repo is False: + os.system(f"rm -r {args.temp_ckpt_folder}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--is_public_repo", + required=False, + default=True, + type=lambda x: (str(x).lower() == "true"), + help="If the model is available for download on huggingface.", + ) + parser.add_argument( + "--hf_model", + type=str, + required=False, + default="openai/whisper-tiny", + help="Huggingface model name. Example: openai/whisper-tiny", + ) + parser.add_argument( + "--ckpt_dir", + type=str, + required=False, + default=".", + help="Folder with the pytorch_model.bin file", + ) + parser.add_argument( + "--temp_ckpt_folder", + type=str, + required=False, + default="temp_dir", + help="Path to create a temporary folder containing the model and related files needed for inference", + ) + parser.add_argument( + "--language", + type=str, + required=False, + default="hi", + help="Two letter language code for the transcription language, e.g. use 'hi' for Hindi. This helps initialize the tokenizer.", + ) + parser.add_argument( + "--eval_datasets", + type=str, + nargs="+", + required=True, + default=[], + help="List of datasets to evaluate the model on.", + ) + parser.add_argument( + "--device", + type=int, + required=False, + default=0, + help="The device to run the pipeline on. -1 for CPU, 0 for the first GPU (default) and so on.", + ) + parser.add_argument( + "--batch_size", + type=int, + required=False, + default=16, + help="Number of samples to go through each streamed batch.", + ) + parser.add_argument( + "--output_dir", + type=str, + required=False, + default="predictions_dir", + help="Output directory for the predictions and hypotheses generated.", + ) + + args = parser.parse_args() + main(args) diff --git a/apps/whisper_fine_tuning/src/core/train/README.md b/apps/whisper_fine_tuning/src/core/train/README.md new file mode 100644 index 0000000..96313c2 --- /dev/null +++ b/apps/whisper_fine_tuning/src/core/train/README.md @@ -0,0 +1,67 @@ +# Whisper Training Module + +This package contains the training entry points and supporting utilities for fine-tuning Whisper models after the audio corpora have been promoted to the "silver" stage. + +## Contents + +``` +core/train/ +├── README.md # This file +├── main_train.py # Primary CLI for LoRA-based Whisper fine-tuning +├── main_train_nemo.py # Alternative CLI for NeMo RNNT experiments +└── core/ # Training configs, helpers, and Trainer implementation +``` + +## Prerequisites +- Python 3.11+ +- Dataset prepared with `src/core/data_prep/main_silver_data_prep.py` and stored under `data/silver/dataset` +- GPU-enabled environment (CUDA required for full training loop) +- Dependencies installed via `poetry install` or `pip install -e .` +- Azure credentials configured if you plan to log to a remote MLflow tracking server + +## Quick Start + +1. Ensure the silver dataset exists: + ```bash + python src/core/data_prep/main_silver_data_prep.py \ + --train_datasets data/raw/training \ + --eval_datasets data/raw/evaluation \ + --test_datasets data/raw/testing + ``` +2. Launch training with the primary CLI: + ```bash + python src/core/train/main_train.py \ + --model_name openai/whisper-large-v2 \ + --dataset data/silver/dataset \ + --output_dir output_model_dir \ + --apply_lora True + ``` +3. Monitor MLflow for run metadata, metrics, and artifacts. + +## CLI Overview (`main_train.py`) +- `--model_name`: Hugging Face identifier for the base Whisper model. +- `--dataset`: Path to the on-disk Hugging Face dataset produced during data prep. +- `--language`: Friendly label used in the auto-generated MLflow experiment name. +- `--apply_lora`: Toggle LoRA adapters (defaults to `True`). +- `--train_strategy`: Accepts `steps` or `epoch`; controls scheduler setup. +- `--experiment_name`: Optional MLflow experiment override; otherwise auto-generated. +- Additional arguments mirror those in the existing training scripts (e.g., sampling rate, num_proc, output_dir). + +## Configuration Notes +- LoRA hyperparameters and trainer defaults live under `core/train/core/config.py`. +- Adjust training behaviors by modifying `ModelLoraConfig` or `TrainingArgsConfig`. +- Update `core/train/core/train.py` if you need custom callbacks, evaluation loops, or logging tweaks. + +## Logging and Artifacts +- Each run logs arguments and environment info via the Python logger. +- MLflow captures metrics, parameters, and checkpoints saved to `output_model_dir`. +- GPU diagnostics (device count, CUDA version) are emitted at startup to help verify hardware availability. + +## Troubleshooting +- **CUDA not available**: Confirm drivers and the correct device are visible (`nvidia-smi`). +- **Dataset schema issues**: Re-run the silver data prep CLI to ensure audio and transcripts align. +- **Import errors** from nested packages: The scripts programmatically extend `sys.path`; verify you execute them from the repository root. + +## Next Steps +- For Azure ML execution, wire `python deployment/training/job_train.py` into your job definition. +- Use `main_train_nemo.py` when experimenting with NeMo RNNT models or alternative architectures. diff --git a/apps/whisper_fine_tuning/src/core/train/core/conda.yaml b/apps/whisper_fine_tuning/src/core/train/core/conda.yaml new file mode 100644 index 0000000..812510a --- /dev/null +++ b/apps/whisper_fine_tuning/src/core/train/core/conda.yaml @@ -0,0 +1,35 @@ +channels: +- conda-forge +dependencies: +- python=3.10.14 +- pip<=24.2 +- pip: + - torch==2.2.2 + - torchvision==0.17.2 + - transformers==4.44.0 + - mlflow==2.14.3 + - accelerate==0.33.0 + - bitsandbytes==0.43.3 + - cffi==1.17.0 + - dill==0.3.8 + - flash-attn==2.5.5 + - google-api-core==2.19.2 + - jaraco-context==5.3.0 + - jaraco-functools==4.0.1 + - jaraco-text==3.12.1 + - more-itertools==10.3.0 + - numpy==1.23.5 + - nvidia-ml-py==12.560.30 + - onnxruntime-training==1.18.0 + - onnxruntime==1.17.3 + - ordered-set==4.1.0 + - packaging==24.1 + - pandas==2.2.2 + - protobuf==3.20.3 + - pynvml==11.5.3 + - pyyaml==6.0.2 + - requests==2.32.3 + - scikit-learn==1.5.2 + - scipy==1.14.0 + - sentencepiece==0.1.99 +name: mlflow-env diff --git a/apps/whisper_fine_tuning/src/core/train/core/config.py b/apps/whisper_fine_tuning/src/core/train/core/config.py new file mode 100644 index 0000000..92e33b9 --- /dev/null +++ b/apps/whisper_fine_tuning/src/core/train/core/config.py @@ -0,0 +1,153 @@ +from typing import Any, Dict + +import yaml +from pydantic import Field +from pydantic.dataclasses import dataclass + + +@dataclass +class RootConfig: + """Base class for managing configurations.""" + + @classmethod + def load_from_yaml(cls, filepath: str): + """Load configuration from a YAML file.""" + with open(filepath, "r") as yaml_file: + yaml_data = yaml.safe_load(yaml_file) + + return cls(**yaml_data) + + @classmethod + def to_dict(self) -> Dict[str, Any]: + """Convert the TrainingArgsConfig to a dictionary.""" + return { + field.name: getattr(self, field.name) + for field in self.__dataclass_fields__.values() + } + + +@dataclass +class ModelingConfig(RootConfig): + """Configuration for the Modeling Process.""" + + model_name: str = "" + evaluation_metric: str = "" + track_experiment: bool = False + experiment_name: str = "" + run_name: str = "" + task: str = "transcribe" + + +@dataclass +class DeploymentConfig(ModelingConfig): + metric_order: str = "" + mode: str = "last" + environment: str = "sandbox" + + +@dataclass +class DeepSpeedConfig(RootConfig): + fp16: Dict[str, Any] = Field( + default_factory=lambda: { + "enabled": "auto", + "loss_scale": 0, + "loss_scale_window": 1000, + "initial_scale_power": 16, + "hysteresis": 2, + "min_loss_scale": 1, + } + ) + + optimizer: Dict[str, Any] = Field( + default_factory=lambda: { + "type": "AdamW", + "params": { + "lr": "auto", + "betas": "auto", + "eps": "auto", + "weight_decay": "auto", + }, + } + ) + + scheduler: Dict[str, Any] = Field( + default_factory=lambda: { + "type": "WarmupLR", + "params": { + "warmup_min_lr": "auto", + "warmup_max_lr": "auto", + "warmup_num_steps": "auto", + }, + } + ) + + zero_optimization: Dict[str, Any] = Field( + default_factory=lambda: { + "stage": 2, + "offload_optimizer": {"device": "cpu", "pin_memory": True}, + "allgather_partitions": True, + "allgather_bucket_size": 2e8, + "overlap_comm": True, + "reduce_scatter": True, + "reduce_bucket_size": 2e8, + "contiguous_gradients": True, + } + ) + + # gradient_accumulation_steps: str = "auto" + gradient_clipping: str = "auto" + steps_per_print: int = 1000 + train_batch_size: int = 1 + # train_micro_batch_size_per_gpu: str = "auto" + wall_clock_breakdown: bool = False + + +@dataclass +class TrainingArgsConfig(RootConfig): + per_device_train_batch_size: int = 2 + gradient_accumulation_steps: int = 8 + per_device_eval_batch_size: int = 4 + eval_accumulation_steps: int = -1 + gradient_checkpointing: bool = True + warmmup_ratio: float = 0.1 + optim: str = "adamw_bnb_8bit" + fp16: bool = True + learning_rate: float = 5e-6 + lr_scheduler_type: str = "linear" + # Remove max_steps so that the number of steps is calculated based on epochs and dataset length. + max_steps: int = -1 # Total training steps. Overrides epochs if positive. + logging_steps: int = 10 + warmup_steps: int = 50 + ddp_find_unused_parameters: bool = False + eval_strategy: str = "epoch" # Calculate metrics per epoch. + save_strategy: str = "epoch" + logging_strategy: str = "epoch" # Log training stats per steps. + load_best_model_at_end: bool = ( + True # Requires saving steps to be a round multiple of evaluation steps. + ) + num_train_epochs: int = 500 + predict_with_generate: bool = True + greater_is_better = False + + # deepspeed: DeepSpeedConfig = DeepSpeedConfig() + + +@dataclass +class ModelLoraConfig: + task_type: str = "CAUSAL_LM" + r: int = 64 + lora_alpha: int = 128 + lora_dropout: float = 0.05 + target_modules: list = Field( + default_factory=lambda: [ + "q_proj", + "k_proj", + "v_proj", + "o_proj", + "gate_proj", + "up_proj", + "down_proj", + "lm_head", + ] + ) + bias: str = "none" diff --git a/apps/whisper_fine_tuning/src/core/train/core/train.py b/apps/whisper_fine_tuning/src/core/train/core/train.py new file mode 100644 index 0000000..09ab547 --- /dev/null +++ b/apps/whisper_fine_tuning/src/core/train/core/train.py @@ -0,0 +1,282 @@ +import os +import sys + +sys.path.append(os.path.join(os.path.dirname(__file__), "..")) +import logging +from dataclasses import asdict +from datetime import datetime +from typing import Any, Callable, Dict, Optional + +import evaluate +import mlflow +import torch +import transformers +from datasets import Dataset +from peft import LoraConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training +from transformers import ( + DataCollatorForSeq2Seq, + Seq2SeqTrainer, + Seq2SeqTrainingArguments, +) + +from apps.whisper_fine_tuning.src.core.train.core.config import ModelLoraConfig, TrainingArgsConfig + +log_file = "training_console.log" +# Redirect stdout and stderr +sys.stdout = open(log_file, "w") +sys.stderr = sys.stdout + +# Set up logging to file as well as console +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(message)s", + handlers=[ + logging.FileHandler(log_file), + logging.StreamHandler(sys.__stdout__), # still print to console + ], +) + +logger = logging.getLogger(__name__) + + +class Trainer: + def __init__( + self, + model_name: str, + model: transformers.PreTrainedModel, + tokenizer: transformers.PreTrainedTokenizer, + processor, # external processor containing feature extraction logic + output_dir: str, + # signature: str, + prompt_template: str, + apply_lora: bool = True, + config: Optional[TrainingArgsConfig] = None, + lora_config: Optional[ModelLoraConfig] = None, + use_conda_yaml: bool = False, + ): + torch.cuda.empty_cache() + os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" + + self._model_name = model_name + self.model_original = model + self.tokenizer = tokenizer + self.processor = processor + self.output_dir = output_dir + # self.signature = signature + self.prompt_template = prompt_template + self.apply_lora = apply_lora + self.config = config.__dict__ if config else {} + self.lora_config = lora_config.__dict__ if lora_config else {} + self._use_conda_yaml = use_conda_yaml + + def compute_metrics( + self, + pred, + do_normalize_eval: bool = False, + normalizer: Optional[Callable] = None, + ) -> Dict: + """Compute evaluation metrics (WER).""" + metric = evaluate.load("wer") + + pred_ids = pred.predictions + label_ids = pred.label_ids + + # Replace -100 with pad_token_id + label_ids[label_ids == -100] = self.tokenizer.pad_token_id + + pred_str = self.tokenizer.batch_decode(pred_ids, skip_special_tokens=True) + label_str = self.tokenizer.batch_decode(label_ids, skip_special_tokens=True) + + if do_normalize_eval and normalizer: + pred_str = [normalizer(s) for s in pred_str] + label_str = [normalizer(s) for s in label_str] + + wer = 100 * metric.compute(predictions=pred_str, references=label_str) + return {"wer": wer} + + def train_model( + self, + train_dataset: Dataset, + eval_dataset: Dataset, + data_collator: DataCollatorForSeq2Seq, + ) -> None: + """ + Train the model using the given train and eval datasets. + """ + + # Prepare model for training with PEFT + model = prepare_model_for_kbit_training(self.model_original) + + # Apply LoRA modifications if enabled + if self.apply_lora: + lora_cfg = LoraConfig( + r=self.lora_config.get("r", 64), + lora_alpha=self.lora_config.get("lora_alpha", 128), + target_modules=self.lora_config.get( + "target_modules", + ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"], + ), + lora_dropout=self.lora_config.get("lora_dropout", 0.05), + bias=self.lora_config.get("bias", "none"), + ) + model = get_peft_model(model, lora_cfg) + model.print_trainable_parameters() + + # Set up training arguments + training_args = Seq2SeqTrainingArguments( + output_dir=self.config.get("output_dir", self.output_dir), + per_device_train_batch_size=self.config.get( + "per_device_train_batch_size", 8 + ), + gradient_accumulation_steps=self.config.get( + "gradient_accumulation_steps", 1 + ), + learning_rate=self.config.get("learning_rate", 1.75e-5), + warmup_steps=self.config.get("warmup_steps", 50), + num_train_epochs=self.config.get("num_train_epochs", 3), + fp16=self.config.get("fp16", True), + per_device_eval_batch_size=self.config.get("per_device_eval_batch_size", 8), + generation_max_length=self.config.get("generation_max_length", 128), + logging_strategy="epoch", + logging_steps=self.config.get("logging_steps", 25), + remove_unused_columns=False, + label_names=["labels"], + report_to="none", + max_steps=self.config.get("max_steps", -1), + ) + + logger.info("training started") + + trainer = Seq2SeqTrainer( + args=training_args, + model=model, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + data_collator=data_collator, + tokenizer=self.processor.feature_extractor, + compute_metrics=self.compute_metrics, + ) + + num_epochs = training_args.num_train_epochs + batch_size = training_args.per_device_train_batch_size + grad_accum = training_args.gradient_accumulation_steps + + steps_per_epoch = (len(train_dataset) + (batch_size * grad_accum) - 1) // ( + batch_size * grad_accum + ) + total_steps = num_epochs * steps_per_epoch + + logger.info( + f"training will run for {num_epochs} epochs and approximately {total_steps} total training steps (around {steps_per_epoch} steps per epoch)." + ) + + mlflow.start_run() + start_time = datetime.now() + logger.info(f"training started at {start_time}") + logger.info(f"run id: {mlflow.active_run().info.run_id}") + + trainer_stats = trainer.train() + + end_time = datetime.now() + logger.info(f"training finished at {end_time}") + logger.info(f"total training time: {end_time - start_time}") + + self.log_params_from_dict(self.config) + self.log_params_from_dict(asdict(lora_cfg)) + + metrics = trainer_stats.metrics + mlflow.log_metrics(metrics) + + logger.info("saving the fine-tuned model locally...") + model.save_pretrained(self.output_dir) + self.tokenizer.save_pretrained(self.output_dir) + self.processor.save_pretrained(self.output_dir) + logger.info( + f"model, tokenizer, and processor successfully saved to {self.output_dir}." + ) + + if self.apply_lora: + # Define the directory to save artifacts + adaptor_dir = "/tmp/model-output-peft" + os.makedirs(adaptor_dir, exist_ok=True) + + trainer.save_model(adaptor_dir) + + # Adaptor directory on your local filesystem + merged_model = PeftModel.from_pretrained(self.model_original, adaptor_dir) + merged_model = merged_model.merge_and_unload() + + logger.info(self.model_original) + logger.info(merged_model) + + if self._use_conda_yaml: + mlflow.transformers.log_model( + transformers_model={ + "model": merged_model, + "tokenizer": self.tokenizer, + "feature_extractor": self.processor.feature_extractor, + }, + # signature=self.signature, + artifact_path="model", + conda_env="conda.yaml", + ) + else: + mlflow.transformers.log_model( + transformers_model={ + "model": merged_model, + "tokenizer": self.tokenizer, + "feature_extractor": self.processor.feature_extractor, + }, + artifact_path="model", + ) + + # Saving the adapter + mlflow.transformers.log_model( + transformers_model={ + "model": trainer.model, + "tokenizer": self.tokenizer, + "feature_extractor": self.processor.feature_extractor, + }, + artifact_path="adapter_peft", + ) + + else: + mlflow.transformers.log_model( + transformers_model={ + "model": trainer.model, + "tokenizer": self.tokenizer, + }, + artifact_path="model", + ) + + mlflow.log_artifact(log_file) + + mlflow.end_run() + + def log_params_from_dict( + self, config: Dict[str, Any], parent_key: str = "" + ) -> None: + """ + Given a dictionary of parameters, logs non-dictionary values to MLflow. + Ignores nested dictionaries. + + Args: + config (Dict[str, any]): The dictionary of parameters to log. + parent_key (str): Used to prefix keys (for nested logging). + """ + max_length = 199 # max length of a parameter value to log + for key, value in config.items(): + if isinstance(value, dict): + continue + elif isinstance(value, list): + full_key = f"{parent_key}.{key}" if parent_key else key + value = ( + value if len(str(value)) < max_length else str(value)[:max_length] + ) + mlflow.log_param(full_key, ",".join(map(str, value))) + else: + full_key = f"{parent_key}.{key}" if parent_key else key + value = ( + value if len(str(value)) < max_length else str(value)[:max_length] + ) + mlflow.log_param(full_key, value) diff --git a/apps/whisper_fine_tuning/src/core/train/core/train_nemo.py b/apps/whisper_fine_tuning/src/core/train/core/train_nemo.py new file mode 100644 index 0000000..8ff6034 --- /dev/null +++ b/apps/whisper_fine_tuning/src/core/train/core/train_nemo.py @@ -0,0 +1,431 @@ +import json +import logging +from contextlib import nullcontext +from dataclasses import asdict, dataclass +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, Optional, Sequence + +import mlflow +import pytorch_lightning as pl +import torch +from datasets import Dataset +from nemo.collections.asr.models import EncDecRNNTBPEModel +from omegaconf import OmegaConf + +logger = logging.getLogger(__name__) + +try: + from nemo.collections.common.parts.mixins.adapter_mixins import LoraConfig as NemoLoraConfig +except ImportError: + NemoLoraConfig = None + + +@dataclass +class RNNTTrainerConfig: + max_epochs: int = 3 + accumulate_grad_batches: int = 1 + gradient_clip_val: float = 1.0 + precision: str = "16-mixed" + accelerator: Optional[str] = None + devices: Optional[int] = None + strategy: Optional[str] = None + log_every_n_steps: int = 10 + enable_checkpointing: bool = True + deterministic: bool = False + limit_train_batches: Optional[float] = None + limit_val_batches: Optional[float] = None + + +@dataclass +class RNNTDataConfig: + train_batch_size: int = 8 + val_batch_size: int = 4 + num_workers: int = 4 + sample_rate: int = 16000 + max_duration: Optional[float] = None + min_duration: Optional[float] = None + pin_memory: bool = True + augmentor: Optional[Dict[str, Any]] = None + + +@dataclass +class RNNTOptimConfig: + learning_rate: float = 1e-4 + weight_decay: Optional[float] = None + warmup_steps: Optional[int] = None + + +@dataclass +class RNNTLoraConfig: + r: int = 8 + lora_alpha: int = 32 + lora_dropout: float = 0.1 + target_modules: Optional[Sequence[str]] = None + use_dora: bool = False + adapter_name: str = "rnnt_lora" + +def create_manifest_from_dataset( + dataset: Dataset, + manifest_path: str, + audio_column: str, + text_column: str, + sample_rate: int, +) -> str: + """Write a NeMo-compatible JSONL manifest from a Hugging Face dataset.""" + manifest_file = Path(manifest_path) + manifest_file.parent.mkdir(parents=True, exist_ok=True) + + print("*********") + + with manifest_file.open("w", encoding="utf-8") as handle: + for row in dataset: + audio_path = row["audio_path"] + if not audio_path: + raise ValueError("Each row must include an audio path for NeMo manifests.") + + duration = None + # Prefer explicit columns produced during data prep + if "duration" in row and row["duration"] is not None: + duration = float(row["duration"]) + elif "input_length" in row and row["input_length"] is not None: + duration = float(row["input_length"]) + + # Fall back to metadata from decoded Audio features, if present + audio = row.get(audio_column) + if duration is None and isinstance(audio, dict): + meta = audio + if meta.get("duration"): + duration = float(meta["duration"]) + elif meta.get("array") is not None and meta.get("sampling_rate"): + duration = len(meta["array"]) / float(meta["sampling_rate"]) + + if duration is None: + raise ValueError( + f"Could not determine duration for {audio_path}. " + "Add a 'duration' or 'input_length' column when preparing the dataset." + ) + + entry = { + "audio_filepath": str(Path(audio_path).expanduser().resolve()), + "text": row[text_column], + "duration": duration, + } + handle.write(json.dumps(entry, ensure_ascii=False) + "\n") + + logger.info("Wrote %d rows to %s", len(dataset), manifest_file) + return str(manifest_file.resolve()) + +class NemoRNNTTrainer: + """Fine-tune a NeMo RNNT model with optional LoRA and MLflow logging.""" + + def __init__( + self, + base_checkpoint_path: str, + output_dir: str, + trainer_config: RNNTTrainerConfig, + data_config: RNNTDataConfig, + optim_config: RNNTOptimConfig, + apply_lora: bool = True, + lora_config: Optional[RNNTLoraConfig] = None, + track_experiment: bool = True, + ) -> None: + self.base_checkpoint_path = base_checkpoint_path + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + + self.trainer_config = trainer_config + self.data_config = data_config + self.optim_config = optim_config + self.apply_lora = apply_lora + self.lora_config = lora_config or RNNTLoraConfig() + self.track_experiment = track_experiment + + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.model_name = Path(base_checkpoint_path).stem + self._adapter_name: Optional[str] = None + + def train( + self, + train_manifest: str, + val_manifest: str, + experiment_name: Optional[str] = None, + run_name: Optional[str] = None, + ) -> str: + if self.track_experiment and experiment_name: + mlflow.set_experiment(experiment_name) + + trainer = pl.Trainer(**self._build_trainer_kwargs()) + model = EncDecRNNTBPEModel.restore_from(self.base_checkpoint_path, map_location=self.device) + model.to(self.device) + + self._configure_model(model, train_manifest, val_manifest) + self._maybe_enable_lora(model) + + run_ctx = ( + mlflow.start_run(run_name=run_name or self._default_run_name()) + if self.track_experiment + else nullcontext() + ) + + with run_ctx: + start = datetime.utcnow() + logger.info("Starting NeMo RNNT fine-tune at %s", start.isoformat()) + + if self.track_experiment: + self._log_params() + + trainer.fit(model) + + metrics = self._extract_metrics(trainer) + if self.track_experiment and metrics: + mlflow.log_metrics(metrics) + + artifact_path = self.output_dir / f"{self.model_name}-finetuned.nemo" + model.save_to(str(artifact_path)) + logger.info("Saved fine-tuned NeMo bundle to %s", artifact_path) + + if self.track_experiment: + mlflow.log_artifact(str(artifact_path), artifact_path="model") + + if ( + self.apply_lora + and self._adapter_name + and hasattr(model, "save_adapters") + ): + adapters_dir = self.output_dir / "adapters" + adapters_dir.mkdir(exist_ok=True) + adapter_path = adapters_dir / f"{self._adapter_name}.nemo" + try: + model.save_adapters(str(adapter_path), [self._adapter_name]) + except TypeError: + model.save_adapters(str(adapter_path)) + if self.track_experiment: + mlflow.log_artifact(str(adapter_path), artifact_path="adapters") + logger.info("Saved LoRA adapters to %s", adapter_path) + + end = datetime.utcnow() + logger.info( + "Finished fine-tune at %s (duration %s)", end.isoformat(), end - start + ) + + torch.cuda.empty_cache() + return str(artifact_path) + + def _log_params(self) -> None: + self.log_params_from_dict(asdict(self.trainer_config), parent_key="trainer") + self.log_params_from_dict(asdict(self.data_config), parent_key="data") + self.log_params_from_dict(asdict(self.optim_config), parent_key="optim") + if self.apply_lora: + self.log_params_from_dict(asdict(self.lora_config), parent_key="lora") + + + def _configure_model( + self, + model: EncDecRNNTBPEModel, + train_manifest: str, + val_manifest: str, + ) -> None: + cfg = model.cfg + + train_cfg = cfg.train_ds + train_cfg.manifest_filepath = train_manifest + train_cfg.sample_rate = self.data_config.sample_rate + train_cfg.shuffle = True + train_cfg.is_tarred = False + train_cfg.tarred_audio_filepaths = None + train_cfg.use_lhotse = False + train_cfg.input_cfg = None + if hasattr(train_cfg, "dataloader_params"): + train_cfg.dataloader_params.batch_size = self.data_config.train_batch_size + train_cfg.dataloader_params.num_workers = self.data_config.num_workers + train_cfg.dataloader_params.pin_memory = self.data_config.pin_memory + else: + train_cfg.batch_size = self.data_config.train_batch_size + train_cfg.num_workers = self.data_config.num_workers + train_cfg.pin_memory = self.data_config.pin_memory + if self.data_config.max_duration is not None: + train_cfg.max_duration = self.data_config.max_duration + if self.data_config.min_duration is not None: + train_cfg.min_duration = self.data_config.min_duration + if self.data_config.augmentor is not None: + train_cfg.augmentor = OmegaConf.create(self.data_config.augmentor) + + model.setup_training_data(train_cfg) + + val_cfg = ( + cfg.validation_ds[0] + if isinstance(cfg.validation_ds, (list, tuple)) + else cfg.validation_ds + ) + val_cfg.manifest_filepath = val_manifest + val_cfg.sample_rate = self.data_config.sample_rate + val_cfg.shuffle = False + val_cfg.is_tarred = False + val_cfg.tarred_audio_filepaths = None + val_cfg.use_lhotse = False + val_cfg.input_cfg = None + if hasattr(val_cfg, "dataloader_params"): + val_cfg.dataloader_params.batch_size = self.data_config.val_batch_size + val_cfg.dataloader_params.num_workers = self.data_config.num_workers + val_cfg.dataloader_params.pin_memory = self.data_config.pin_memory + else: + val_cfg.batch_size = self.data_config.val_batch_size + val_cfg.num_workers = self.data_config.num_workers + val_cfg.pin_memory = self.data_config.pin_memory + + print("*****************") + + model.setup_validation_data(val_cfg) + + def _maybe_enable_lora(self, model: EncDecRNNTBPEModel) -> None: + if not self.apply_lora: + return + if NemoLoraConfig is None or not hasattr(model, "enable_lora"): + logger.warning("LoRA support is unavailable in this NeMo build; continuing without adapters.") + self.apply_lora = False + return + + lora_kwargs = dict( + r=self.lora_config.r, + lora_alpha=self.lora_config.lora_alpha, + lora_dropout=self.lora_config.lora_dropout, + ) + if self.lora_config.target_modules: + lora_kwargs["target_modules"] = list(self.lora_config.target_modules) + if self.lora_config.use_dora: + lora_kwargs["use_dora"] = True + + nemo_lora_cfg = NemoLoraConfig(**lora_kwargs) + try: + model.enable_lora(config=nemo_lora_cfg, adapter_name=self.lora_config.adapter_name) + except TypeError: + model.enable_lora(nemo_lora_cfg) + self._adapter_name = self.lora_config.adapter_name + if hasattr(model, "set_enabled_adapters"): + model.set_enabled_adapters([self._adapter_name]) + logger.info("Enabled LoRA adapters: %s", lora_kwargs) + + def _build_trainer_kwargs(self) -> Dict[str, Any]: + accelerator = self.trainer_config.accelerator or ( + "gpu" if torch.cuda.is_available() else "cpu" + ) + devices = self.trainer_config.devices or ( + torch.cuda.device_count() if accelerator == "gpu" else 1 + ) + kwargs: Dict[str, Any] = { + "accelerator": accelerator, + "devices": devices, + "max_epochs": self.trainer_config.max_epochs, + "accumulate_grad_batches": self.trainer_config.accumulate_grad_batches, + "gradient_clip_val": self.trainer_config.gradient_clip_val, + "precision": self.trainer_config.precision, + "log_every_n_steps": self.trainer_config.log_every_n_steps, + "enable_checkpointing": self.trainer_config.enable_checkpointing, + "deterministic": self.trainer_config.deterministic, + "default_root_dir": str(self.output_dir), + } + if self.trainer_config.strategy: + kwargs["strategy"] = self.trainer_config.strategy + if self.trainer_config.limit_train_batches is not None: + kwargs["limit_train_batches"] = self.trainer_config.limit_train_batches + if self.trainer_config.limit_val_batches is not None: + kwargs["limit_val_batches"] = self.trainer_config.limit_val_batches + return kwargs + + @staticmethod + def _extract_metrics(trainer: pl.Trainer) -> Dict[str, float]: + metrics: Dict[str, float] = {} + for key, value in trainer.callback_metrics.items(): + if value is None: + continue + if isinstance(value, (int, float)): + metrics[key] = float(value) + elif hasattr(value, "item"): + try: + metrics[key] = float(value.item()) + except (TypeError, ValueError): + continue + return metrics + + @staticmethod + def log_params_from_dict(config: Dict[str, Any], parent_key: str = "") -> None: + if not mlflow.active_run(): + return + max_length = 199 + for key, value in config.items(): + if isinstance(value, dict): + continue + full_key = f"{parent_key}.{key}" if parent_key else key + if isinstance(value, list): + mlflow.log_param(full_key, ",".join(map(str, value))[:max_length]) + else: + mlflow.log_param(full_key, str(value)[:max_length]) + + def _default_run_name(self) -> str: + return f"{self.model_name}-finetune-{datetime.utcnow().strftime('%Y%m%d-%H%M%S')}" + + +def train_nemo_from_datasets( + train_dataset: Dataset, + val_dataset: Optional[Dataset], + base_checkpoint_path: str, + output_dir: str, + audio_column: str, + text_column: str, + trainer_config: RNNTTrainerConfig, + data_config: RNNTDataConfig, + optim_config: RNNTOptimConfig, + lora_config: Optional[RNNTLoraConfig] = None, + apply_lora: bool = True, + manifest_dir: Optional[str] = None, + validation_split: float = 0.02, + experiment_name: Optional[str] = None, + run_name: Optional[str] = None, + track_experiment: bool = True, +) -> str: + """Convenience wrapper to fine-tune NeMo RNNT models from Hugging Face datasets.""" + if val_dataset is None: + if not 0 < validation_split < 1: + raise ValueError("validation_split must be between 0 and 1 when no validation dataset is provided.") + split = train_dataset.train_test_split(test_size=validation_split, seed=42) + train_dataset, val_dataset = split["train"], split["test"] + + manifest_root = Path(manifest_dir or Path(output_dir) / "manifests") + manifest_root.mkdir(parents=True, exist_ok=True) + + train_manifest = manifest_root / "train_manifest.json" + val_manifest = manifest_root / "val_manifest.json" + + train_manifest = create_manifest_from_dataset( + train_dataset, + str(train_manifest), + audio_column, + text_column, + data_config.sample_rate, + ) + val_manifest = create_manifest_from_dataset( + val_dataset, + str(val_manifest), + audio_column, + text_column, + data_config.sample_rate, + ) + + trainer = NemoRNNTTrainer( + base_checkpoint_path=base_checkpoint_path, + output_dir=output_dir, + trainer_config=trainer_config, + data_config=data_config, + optim_config=optim_config, + apply_lora=apply_lora, + lora_config=lora_config, + track_experiment=track_experiment, + ) + + return trainer.train( + train_manifest=str(train_manifest), + val_manifest=str(val_manifest), + experiment_name=experiment_name, + run_name=run_name, + ) \ No newline at end of file diff --git a/apps/whisper_fine_tuning/src/core/train/main_train.py b/apps/whisper_fine_tuning/src/core/train/main_train.py new file mode 100644 index 0000000..b489412 --- /dev/null +++ b/apps/whisper_fine_tuning/src/core/train/main_train.py @@ -0,0 +1,199 @@ +import os +import sys + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) +import argparse +import logging +from datetime import datetime + +import mlflow +import torch +from datasets import load_from_disk +from transformers import ( + WhisperForConditionalGeneration, + WhisperProcessor, + WhisperTokenizer, +) + +# Fix the path to point to the repository root where 'src' directory is located +repo_root = os.path.join(os.path.dirname(__file__), "..", "..") +sys.path.insert(0, repo_root) + + +from apps.whisper_fine_tuning.src.core.train.core.config import ModelLoraConfig, TrainingArgsConfig +from src.core.data_prep.load_data import DataCollatorSpeechSeq2SeqWithPadding +from apps.whisper_fine_tuning.src.core.train.core.train import Trainer + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def main(): + parser = argparse.ArgumentParser( + description="fine-tuning script for whisper models of various sizes." + ) + parser.add_argument( + "--model_name", + type=str, + default="openai/whisper-large-v2", + help="huggingface model name to fine-tune. eg: openai/whisper-small", + ) + + parser.add_argument( + "--apply_lora", + type=bool, + default=True, + ) + + parser.add_argument( + "--language", + type=str, + default="Hindi", + help="language the model is being adapted to in camel case.", + ) + parser.add_argument( + "--sampling_rate", type=int, default=16000, help="sampling rate of audios." + ) + parser.add_argument( + "--num_proc", + type=int, + default=2, + help="number of parallel jobs to run. helps parallelize the dataset prep stage.", + ) + parser.add_argument( + "--train_strategy", + type=str, + default="steps", + help="training strategy. choose between steps and epoch.", + ) + + parser.add_argument( + "--resume_from_ckpt", + type=str, + default=None, + help="path to a trained checkpoint to resume training from.", + ) + parser.add_argument( + "--output_dir", + type=str, + default="output_model_dir", + help="output directory for the checkpoints generated.", + ) + parser.add_argument( + "--dataset", + type=str, + default="steps", + help="list of datasets to be used for training.", + ) + + # Add experiment name argument + parser.add_argument( + "--experiment_name", + type=str, + default=None, + help="MLflow experiment name for tracking runs. If not provided, will be auto-generated.", + ) + + args = parser.parse_args() + + if args.train_strategy not in ["steps", "epoch"]: + raise ValueError('the train strategy should be either "steps" or "epoch".') + + # Generate experiment name if not provided + if args.experiment_name is None: + model_short = args.model_name.split('/')[-1] # e.g., "whisper-large-v2" + language_short = args.language.lower() + lora_suffix = "-lora" if args.apply_lora else "" + timestamp = datetime.now().strftime('%Y%m%d') + args.experiment_name = f"whisper-{language_short}-{model_short}{lora_suffix}-{timestamp}" + + # Set MLflow experiment + mlflow.set_experiment(args.experiment_name) + logger.info(f"Using MLflow experiment: {args.experiment_name}") + + # Log arguments for transparency + logger.info("\n\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n\n") + logger.info("arguments of interest:") + logger.info(vars(args)) + logger.info("\n\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n\n") + + gradient_checkpointing = True + freeze_feature_encoder = False + freeze_encoder = False + + gpu_available = torch.cuda.is_available() + torch.cuda.device(0) + logger.info(f"cuda available: {gpu_available}") + if gpu_available: + logger.info(f"cuda version: {torch.version.cuda}") + logger.info(f"number of cuda devices: {torch.cuda.device_count()}") + current_device = torch.cuda.current_device() + logger.info(f"current cuda device: {current_device}") + logger.info(f"cuda device name: {torch.cuda.get_device_name(current_device)}") + else: + logger.info("cuda is not available. please check your gpu configuration.") + + # Load models and processors. + tokenizer = WhisperTokenizer.from_pretrained(args.model_name, task="transcribe") + processor = WhisperProcessor.from_pretrained(args.model_name, task="transcribe") + + logger.info("loading model...") + model = WhisperForConditionalGeneration.from_pretrained( + args.model_name + ) # , load_in_8bit=True, device_map="auto") + + if model.config.decoder_start_token_id is None: + raise ValueError( + "ensure that `config.decoder_start_token_id` is correctly defined" + ) + + if freeze_feature_encoder: + model.freeze_feature_encoder() + + if freeze_encoder: + model.freeze_encoder() + model.model.encoder.gradient_checkpointing = False + + if gradient_checkpointing: + model.config.use_cache = False + + model.config.forced_decoder_ids = None + model.config.suppress_tokens = [] + + # Load again the dataset to ensure it's in the correct format + raw_dataset = load_from_disk(args.dataset) + data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor) + + # Debug: Log details of the first sample from the training set + sample = raw_dataset["train"][0] + logger.info("sample sentence: %s", sample["sentence"]) + + # After data_collator is created and dataset is prepared + training_args_config = TrainingArgsConfig() # or load from file/args + lora_config = ModelLoraConfig() # or load from file/args + + trainer = Trainer( + model_name=args.model_name, + model=model, + tokenizer=tokenizer, + processor=processor, + output_dir=args.output_dir, + prompt_template="", + apply_lora=args.apply_lora, + config=training_args_config, + lora_config=lora_config, + ) + + try: + trainer.train_model( + train_dataset=raw_dataset["train"], + eval_dataset=raw_dataset["eval"] if "eval" in raw_dataset else None, + data_collator=data_collator, + ) + except Exception as e: + logger.error(f"erro durante o treinamento: {e}", exc_info=True) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/apps/whisper_fine_tuning/src/core/train/main_train_nemo.py b/apps/whisper_fine_tuning/src/core/train/main_train_nemo.py new file mode 100644 index 0000000..3df4741 --- /dev/null +++ b/apps/whisper_fine_tuning/src/core/train/main_train_nemo.py @@ -0,0 +1,219 @@ + +import os +os.environ.setdefault("LIGHTNING_DISABLE_ADLFS_IMPORT", "1") +os.environ.setdefault("NUMBA_CUDA_ARCH_LIST", "70") # use "75" if you prefer +os.environ.setdefault("NUMBA_CUDA_USE_NVIDIA_BINDING", "1") + +import argparse +import logging + +import sys +from datetime import datetime +from pathlib import Path +from typing import Optional + +import mlflow +import pytorch_lightning as pl +import torch +if torch.cuda.is_available(): + torch.set_float32_matmul_precision("high") + torch.backends.cuda.matmul.allow_tf32 = True +from datasets import load_from_disk +from huggingface_hub import hf_hub_download + +repo_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")) +if repo_root not in sys.path: + sys.path.insert(0, repo_root) + +from apps.whisper_fine_tuning.src.core.train.core.train_nemo import ( + RNNTDataConfig, + RNNTLoraConfig, + RNNTOptimConfig, + RNNTTrainerConfig, + train_nemo_from_datasets, +) + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +import faulthandler; faulthandler.enable() + +def resolve_checkpoint_path( + checkpoint_path: Optional[str], + repo_id: str, + filename: str, + token: Optional[str], +) -> str: + if checkpoint_path: + path = Path(checkpoint_path).expanduser() + if not path.exists(): + raise FileNotFoundError(f"Checkpoint not found at {path}") + return str(path) + logger.info("Downloading %s (%s) from Hugging Face Hub...", repo_id, filename) + return hf_hub_download(repo_id=repo_id, filename=filename, token=token) + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Fine-tune NVIDIA Parakeet RNNT with NeMo + LoRA." + ) + parser.add_argument("--dataset_path", type=str, default="data/silver/dataset") + parser.add_argument("--audio_column", type=str, default="audio") + parser.add_argument("--text_column", type=str, default="sentence") + parser.add_argument("--output_dir", type=str, default="output_model_dir/nemo_rnnt") + parser.add_argument("--manifest_dir", type=str, default=None) + parser.add_argument("--validation_split", type=float, default=0.02) + + parser.add_argument("--max_epochs", type=int, default=500) + parser.add_argument("--train_batch_size", type=int, default=4) + parser.add_argument("--eval_batch_size", type=int, default=4) + parser.add_argument("--grad_accumulation", type=int, default=4) + parser.add_argument("--gradient_clip_val", type=float, default=1.0) + parser.add_argument("--precision", type=str, default="16-mixed") + parser.add_argument("--accelerator", type=str, default=None) + parser.add_argument("--devices", type=int, default=None) + parser.add_argument("--strategy", type=str, default=None) + parser.add_argument("--limit_train_batches", type=float, default=None) + parser.add_argument("--limit_val_batches", type=float, default=None) + parser.add_argument("--log_every_n_steps", type=int, default=10) + parser.add_argument("--deterministic", action="store_true") + parser.add_argument("--disable_checkpointing", action="store_true") + + parser.add_argument("--learning_rate", type=float, default=3e-5) + parser.add_argument("--warmup_steps", type=int, default=50) + parser.add_argument("--weight_decay", type=float, default=0.0) + parser.add_argument("--num_workers", type=int, default=4) + parser.add_argument("--sample_rate", type=int, default=16000) + + parser.add_argument("--apply_lora", dest="apply_lora", action="store_true", default=True) + parser.add_argument("--no_lora", dest="apply_lora", action="store_false") + parser.add_argument("--lora_r", type=int, default=64) + parser.add_argument("--lora_alpha", type=int, default=128) + parser.add_argument("--lora_dropout", type=float, default=0.05) + parser.add_argument("--lora_target_modules", type=str, default=None) + parser.add_argument("--lora_adapter_name", type=str, default="rnnt_lora") + parser.add_argument("--use_dora", action="store_true") + + parser.add_argument("--checkpoint_path", type=str, default=None) + parser.add_argument( + "--checkpoint_repo", + type=str, + default="nvidia/parakeet-rnnt-110m-da-dk", + ) + parser.add_argument( + "--checkpoint_filename", + type=str, + default="parakeet-rnnt-110m-da-dk.nemo", + ) + parser.add_argument("--hf_token", type=str, default=None) + + parser.add_argument("--experiment_name", type=str, default=None) + parser.add_argument("--run_name", type=str, default=None) + parser.add_argument("--mlflow_tracking_uri", type=str, default=None) + parser.add_argument("--seed", type=int, default=42) + + args = parser.parse_args() + + if args.mlflow_tracking_uri: + mlflow.set_tracking_uri(args.mlflow_tracking_uri) + + if args.seed is not None: + pl.seed_everything(args.seed, workers=True) + + if args.experiment_name is None: + timestamp = datetime.utcnow().strftime("%Y%m%d-%H%M%S") + args.experiment_name = f"nemo-rnnt-{timestamp}" + mlflow.set_experiment(args.experiment_name) + logger.info("Using MLflow experiment: %s", args.experiment_name) + + logger.info("Arguments: %s", vars(args)) + + gpu_available = torch.cuda.is_available() + logger.info("CUDA available: %s", gpu_available) + if gpu_available: + logger.info("CUDA devices: %d", torch.cuda.device_count()) + logger.info("Current device: %s", torch.cuda.get_device_name(torch.cuda.current_device())) + else: + logger.warning("GPU not detected; training will fall back to CPU.") + + dataset = load_from_disk(args.dataset_path) + train_dataset = dataset["train"] + val_dataset = None + for split in ("eval", "validation", "dev", "test"): + if split in dataset: + val_dataset = dataset[split] + break + + checkpoint_path = resolve_checkpoint_path( + args.checkpoint_path, + repo_id=args.checkpoint_repo, + filename=args.checkpoint_filename, + token=args.hf_token, + ) + + trainer_config = RNNTTrainerConfig( + max_epochs=args.max_epochs, + accumulate_grad_batches=args.grad_accumulation, + gradient_clip_val=args.gradient_clip_val, + precision=args.precision, + accelerator=args.accelerator, + devices=args.devices, + strategy=args.strategy, + log_every_n_steps=args.log_every_n_steps, + enable_checkpointing=not args.disable_checkpointing, + deterministic=args.deterministic, + limit_train_batches=args.limit_train_batches, + limit_val_batches=args.limit_val_batches, + ) + + data_config = RNNTDataConfig( + train_batch_size=args.train_batch_size, + val_batch_size=args.eval_batch_size, + num_workers=args.num_workers, + sample_rate=args.sample_rate, + ) + + optim_config = RNNTOptimConfig( + learning_rate=args.learning_rate, + weight_decay=args.weight_decay or None, + warmup_steps=args.warmup_steps if args.warmup_steps > 0 else None, + ) + + target_modules = ( + [module.strip() for module in args.lora_target_modules.split(",") if module.strip()] + if args.lora_target_modules + else None + ) + lora_config = RNNTLoraConfig( + r=args.lora_r, + lora_alpha=args.lora_alpha, + lora_dropout=args.lora_dropout, + target_modules=target_modules, + use_dora=args.use_dora, + adapter_name=args.lora_adapter_name, + ) + + artifact_path = train_nemo_from_datasets( + train_dataset=train_dataset, + val_dataset=val_dataset, + base_checkpoint_path=checkpoint_path, + output_dir=args.output_dir, + audio_column=args.audio_column, + text_column=args.text_column, + trainer_config=trainer_config, + data_config=data_config, + optim_config=optim_config, + lora_config=lora_config, + apply_lora=args.apply_lora, + manifest_dir=args.manifest_dir, + validation_split=args.validation_split, + experiment_name=args.experiment_name, + run_name=args.run_name, + track_experiment=True, + ) + + logger.info("Fine-tuned model saved to %s", artifact_path) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/apps/whisper_fine_tuning/src/utils/__init__.py b/apps/whisper_fine_tuning/src/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/apps/whisper_fine_tuning/src/utils/utils.py b/apps/whisper_fine_tuning/src/utils/utils.py new file mode 100644 index 0000000..7f49880 --- /dev/null +++ b/apps/whisper_fine_tuning/src/utils/utils.py @@ -0,0 +1,18 @@ +import pandas as pd +from IPython.display import HTML, display + + +def display_table(dataset_or_sample): + # A helper fuction to display a Transformer dataset or single sample contains multi-line string nicely + pd.set_option("display.max_colwidth", None) + pd.set_option("display.width", None) + pd.set_option("display.max_rows", None) + + if isinstance(dataset_or_sample, dict): + df = pd.DataFrame(dataset_or_sample, index=[0]) + else: + df = pd.DataFrame(dataset_or_sample) + + html = df.to_html().replace("\\n", "
          ") + styled_html = f""" {html}""" + display(HTML(styled_html)) From 3d2d02a600e598559cacd6565e1205a1824b8a15 Mon Sep 17 00:00:00 2001 From: Karina Assini Date: Mon, 3 Nov 2025 19:39:08 +0000 Subject: [PATCH 2/4] fix: fixing and testing training script --- .../src/core/data_prep/main_silver_data_prep.py | 2 +- .../src/core/train/{core => }/conda.yaml | 0 .../src/core/train/{core => }/config.py | 0 .../src/core/train/main_train.py | 15 +++++++++------ .../src/core/train/{core => }/train.py | 2 +- .../src/core/train/{core => }/train_nemo.py | 0 6 files changed, 11 insertions(+), 8 deletions(-) rename apps/whisper_fine_tuning/src/core/train/{core => }/conda.yaml (100%) rename apps/whisper_fine_tuning/src/core/train/{core => }/config.py (100%) rename apps/whisper_fine_tuning/src/core/train/{core => }/train.py (99%) rename apps/whisper_fine_tuning/src/core/train/{core => }/train_nemo.py (100%) diff --git a/apps/whisper_fine_tuning/src/core/data_prep/main_silver_data_prep.py b/apps/whisper_fine_tuning/src/core/data_prep/main_silver_data_prep.py index 5453160..81fc822 100644 --- a/apps/whisper_fine_tuning/src/core/data_prep/main_silver_data_prep.py +++ b/apps/whisper_fine_tuning/src/core/data_prep/main_silver_data_prep.py @@ -157,7 +157,7 @@ def main(): logger.info("DATASET PREPARATION IN PROGRESS...") raw_dataset = data_prep.prepare() - save_path = "./data/silver/dataset" + save_path = "apps/whisper_fine_tuning/data/silver/dataset" raw_dataset.save_to_disk(save_path) logger.info(f"Dataset saved locally at {save_path}.") diff --git a/apps/whisper_fine_tuning/src/core/train/core/conda.yaml b/apps/whisper_fine_tuning/src/core/train/conda.yaml similarity index 100% rename from apps/whisper_fine_tuning/src/core/train/core/conda.yaml rename to apps/whisper_fine_tuning/src/core/train/conda.yaml diff --git a/apps/whisper_fine_tuning/src/core/train/core/config.py b/apps/whisper_fine_tuning/src/core/train/config.py similarity index 100% rename from apps/whisper_fine_tuning/src/core/train/core/config.py rename to apps/whisper_fine_tuning/src/core/train/config.py diff --git a/apps/whisper_fine_tuning/src/core/train/main_train.py b/apps/whisper_fine_tuning/src/core/train/main_train.py index b489412..4c4a9cd 100644 --- a/apps/whisper_fine_tuning/src/core/train/main_train.py +++ b/apps/whisper_fine_tuning/src/core/train/main_train.py @@ -16,13 +16,15 @@ ) # Fix the path to point to the repository root where 'src' directory is located -repo_root = os.path.join(os.path.dirname(__file__), "..", "..") -sys.path.insert(0, repo_root) +from pathlib import Path +APP_ROOT = Path(__file__).resolve().parents[3] # …/language-creation/apps/whisper_fine_tuning +if str(APP_ROOT) not in sys.path: + sys.path.insert(0, str(APP_ROOT)) +print(sys.path) - -from apps.whisper_fine_tuning.src.core.train.core.config import ModelLoraConfig, TrainingArgsConfig +from config import ModelLoraConfig, TrainingArgsConfig from src.core.data_prep.load_data import DataCollatorSpeechSeq2SeqWithPadding -from apps.whisper_fine_tuning.src.core.train.core.train import Trainer +from train import Trainer # Configure logging logging.basicConfig(level=logging.INFO) @@ -30,6 +32,7 @@ def main(): + print("Starting main training script...") parser = argparse.ArgumentParser( description="fine-tuning script for whisper models of various sizes." ) @@ -49,7 +52,7 @@ def main(): parser.add_argument( "--language", type=str, - default="Hindi", + default="", help="language the model is being adapted to in camel case.", ) parser.add_argument( diff --git a/apps/whisper_fine_tuning/src/core/train/core/train.py b/apps/whisper_fine_tuning/src/core/train/train.py similarity index 99% rename from apps/whisper_fine_tuning/src/core/train/core/train.py rename to apps/whisper_fine_tuning/src/core/train/train.py index 09ab547..2da3ad7 100644 --- a/apps/whisper_fine_tuning/src/core/train/core/train.py +++ b/apps/whisper_fine_tuning/src/core/train/train.py @@ -19,7 +19,7 @@ Seq2SeqTrainingArguments, ) -from apps.whisper_fine_tuning.src.core.train.core.config import ModelLoraConfig, TrainingArgsConfig +from config import ModelLoraConfig, TrainingArgsConfig log_file = "training_console.log" # Redirect stdout and stderr diff --git a/apps/whisper_fine_tuning/src/core/train/core/train_nemo.py b/apps/whisper_fine_tuning/src/core/train/train_nemo.py similarity index 100% rename from apps/whisper_fine_tuning/src/core/train/core/train_nemo.py rename to apps/whisper_fine_tuning/src/core/train/train_nemo.py From 4a2bb7260b4fa4c9cc24f18e79752c2c9ee2197d Mon Sep 17 00:00:00 2001 From: Karina Assini Date: Mon, 3 Nov 2025 19:49:55 +0000 Subject: [PATCH 3/4] chore: readme improvement --- apps/whisper_fine_tuning/README.md | 309 ++++++------------ .../src/core/data_prep/README.md | 20 +- .../src/core/train/README.md | 7 +- 3 files changed, 111 insertions(+), 225 deletions(-) diff --git a/apps/whisper_fine_tuning/README.md b/apps/whisper_fine_tuning/README.md index d3327ac..fcfd62c 100644 --- a/apps/whisper_fine_tuning/README.md +++ b/apps/whisper_fine_tuning/README.md @@ -1,228 +1,111 @@ -# Azure Logo Fine Tuning Open Source LLM Models - QLora and Lora features implemented - -## Overview -Open-source LLMs are powerful but require fine-tuning for specific tasks like chatbots or content generation. Fine-tuning these models can be expensive due to the need for substantial VRAM. For instance, fully fine-tuning the Llama7B model requires 112GB of VRAM. However, techniques like QLoRA and PEFT can significantly reduce these requirements. - # Whisper Fine-Tuning Pipeline -This repository provides a pipeline for fine-tuning OpenAI's Whisper models on custom audio datasets using LoRA (Low-Rank Adaptation) and PEFT (Parameter-Efficient Fine-Tuning). The solution is designed for scalable training and deployment on Azure Machine Learning. +This project provides an end-to-end workflow for preparing custom audio datasets, fine-tuning OpenAI’s Whisper models with LoRA/PEFT, and operationalizing the resulting checkpoints on Azure ML. -## Features -- Fine-tune Whisper models with LoRA and PEFT -- Custom data loading and preprocessing for speech datasets -- MLflow integration for experiment tracking and model logging (including all console logs as artifacts) -- Azure ML job specification for cloud training -- Modular codebase for easy extension +## Highlights +- LoRA-based fine-tuning scripts that minimize GPU memory requirements. +- Data ingestion utilities that convert raw audio into Hugging Face `datasets` format. +- MLflow integration for experiment tracking and artifact storage. +- Azure ML job definitions for cloud-scale training and evaluation. -## Directory Structure +## Project Layout ``` -src/core/ - config.py # Training and LoRA configuration classes - load_data.py # Data loading and preprocessing - train.py # Trainer class and training logic -deployment/ - job_data.py # Data preparation script - job_train.py # Training script - environment.yml # Conda environment for Azure ML - training_job.yaml # Azure ML job specification -notebooks/ - fine_tuned_usage.ipynb # Example notebook for inference and usage -data/ - dataset_silver/ # Example processed dataset +apps/whisper_fine_tuning/ +├── deployment/ # Azure ML job specs and training entrypoints +├── docs/ # Diagrams and supporting documentation +├── infra/ # Infrastructure-as-code templates +├── notebooks/ # Exploratory analysis and inference notebooks +├── src/core/ # Data prep, training, and evaluation modules +└── data/ # Example datasets (raw/silver) – ignored in git ``` -## Setup -1. **Clone the repository** - ```bash - git clone - cd whisper-fine-tuning - ``` -2. **Prepare your dataset** - - Place your audio datasets in a directory (e.g., `data/train_raw`, `data/evaluation_raw`). - - Ensure datasets are in Hugging Face `datasets` format. - -3. **Create the environment** - +## Quick Start +1. **Install dependencies** + ```bash + poetry env use 3.12 + poetry install + ``` + Activate the environment with `poetry shell` or your preferred virtualenv tool. + +2. **Prepare raw audio** + ```bash + python src/core/data_prep/main_data_prep.py \ + --source_data_dir data/raw/audios/matis \ + --output_data_dir data/raw/training \ + --domain train + ``` + Repeat for `--domain evaluation` and `--domain test`. See `src/core/data_prep/README.md` for more options. + +3. **Generate silver dataset** + ```bash + python src/core/data_prep/main_silver_data_prep.py \ + --train_datasets data/raw/training \ + --eval_datasets data/raw/evaluation \ + --test_datasets data/raw/testing + ``` + +4. **Train Whisper with LoRA** + ```bash + python src/core/train/main_train.py \ + --model_name openai/whisper-large-v2 \ + --dataset data/silver/dataset \ + --language Matis \ + --output_dir output_model_dir \ + --apply_lora True + ``` + +## Running on Azure ML +Submit the packaged job using the provided YAML spec: ```bash -# Use Python 3.12 for the Poetry environment -poetry env use 3.12 - -# Activate the virtual environment -poetry shell - -# Install all project dependencies -poetry install +az ml job create --file deployment/training_job.yaml ``` - -## Usage - -### Data Preparation - -### Data Preparation in Create Data - -For detailed instructions on dataset preparation, please refer to the README in the create_data directory. - - - -### Model Training - -Run the training pipeline: -```bash -python deployment/training/job_train.py --model_name openai/whisper-large-v2 --dataset ./data/silver/dataset --apply_lora True +Customize compute, environment, and inputs inside the YAML before submission. + +## Evaluation & Inference +- Offline evaluation: `python src/core/evaluation/evaluation_process.py --eval_datasets data/raw/testing ...` +- NeMo experiments: `python src/core/train/main_train_nemo.py --dataset_path data/silver/dataset ...` +- Inference notebook: `notebooks/fine_tuned_usage.ipynb` + +## Key CLI Arguments +| Script | Argument | Description | +| --- | --- | --- | +| `main_data_prep.py` | `--source_data_dir` | Folder with `audio_paths` and `text` mappings | +| | `--domain` | `train`, `test`, `evaluation`, or `all` | +| `main_silver_data_prep.py` | `--train_datasets`/`--eval_datasets` | Hugging Face datasets produced by the raw prep stage | +| `main_train.py` | `--model_name` | Base Whisper checkpoint (default `openai/whisper-small`) | +| | `--apply_lora` | Enable/disable LoRA adapters | +| | `--experiment_name` | MLflow experiment name; auto-generated if omitted | + +Full option lists live in each script’s `--help` output. + +## MLflow & Logging +- Runs log configuration, metrics, and console output (see `training_console.log`). +- Checkpoints are written to `output_model_dir/` and can be registered with MLflow or uploaded to Azure. + +## Pre-Commit Hooks ``` - -If you want to load a lightweight model configuration, run the training command below. This command fine-tunes OpenAI's Whisper model with LoRA applied on your specified dataset: - -```bash -python deployment/training/job_train.py --model_name openai/whisper-large-v2 --dataset ./data/silver/dataset --apply_lora True --experiment_name "whisper-mayoruna-v2" - +pip install pre-commit +pre-commit install +pre-commit run --all-files ``` - -This approach helps optimize resource usage while enabling effective model adaptation. - - -- All console and logging output will be saved as an MLflow artifact (`training_console.log`). - - -## Model Evaluating - - -```bash -python src/core/evaluation/evaluation_process.py --is_public_repo False --ckpt_dir "output_model_dir" --temp_ckpt_folder "temp" --eval_datasets data/raw/testing --device 0 --batch_size 16 --output_dir predictions_dir -``` - - -python deployment/training/job_train_nemo.py \ - --dataset_path data/silver/dataset \ - --output_dir nemo_rnnt_da/ \ - - -python deployment/training/job_train_nemo.py \ - --dataset_path data/silver/dataset \ - --output_dir output_model_dir/nemo_rnnt_da \ - --num_workers 0 - - -### Azure ML Training -1. **Configure Azure ML compute and workspace.** -2. **Submit the job:** - ```bash - az ml job create --file deployment/training_job.yaml - ``` - -## Arguments - -### `job_data.py` -- `--model_name`: Hugging Face model name (default: `openai/whisper-small`) -- `--train_datasets`: List of training dataset paths (required) -- `--eval_datasets`: List of evaluation dataset paths (optional) -- `--sampling_rate`: Audio sampling rate (default: 16000) -- `--num_proc`: Number of parallel jobs for data prep (default: 2) -- `--output_dir`: Output directory for processed dataset - -### `job_train.py` -- `--model_name`: Hugging Face model name (default: `openai/whisper-small`) -- `--dataset`: Path to processed dataset (required) -- `--output_dir`: Output directory for checkpoints (default: `output_model_dir`) -- `--apply_lora`: Whether to apply LoRA (default: True) -- `--language`: Language for adaptation (default: Hindi) -- `--sampling_rate`: Audio sampling rate (default: 16000) -- `--num_proc`: Number of parallel jobs for data prep (default: 2) -- `--train_strategy`: Training strategy (`steps` or `epoch`, default: `steps`) - -## Customization -- Modify `src/core/config.py` to adjust training and LoRA parameters. -- Update `deployment/environment.yml` to add/remove dependencies. -- Edit `deployment/training_job.yaml` for Azure ML compute, environment, and output settings. - -## Outputs -- Trained model checkpoints in the specified output directory -- MLflow experiment logs (including all console logs as artifacts) -- Optionally, model artifacts pushed to Hugging Face Hub - -## Inference Example - -See `notebooks/fine_tuned_usage.ipynb` for detailed usage. -To load and use a model logged with MLflow: -```python -import mlflow.transformers - -model_uri = "models:/whisper-fine-tuned/1" # or your own model URI -pipe = mlflow.transformers.load_model(model_uri) -result = pipe("path/to/audio/file.wav") -print(result["text"]) -``` -Or, using the pyfunc interface: -```python -import mlflow.pyfunc -import pandas as pd - -loaded_model = mlflow.pyfunc.load_model(model_uri) -df = pd.DataFrame({"inputs": ["path/to/audio/file.wav"]}) -result = loaded_model.predict(df) -print(result) -``` - - -## Steps to Configure Pre-commit - -1. Install pre-commit (if not already installed): - - ```bash - pip install pre-commit - ``` - -2. Use the `.pre-commit-config.yaml` file in the root of the repository - -3. Install the pre-commit hooks: - - ```bash - pre-commit install - ``` - -4. Run the hooks manually on all files to ensure consistency: - - ```bash - pre-commit run --all-files - ``` - -Integrating pre-commit into your workflow helps maintain consistent code standards and prevents common issues before they enter your codebase. - +Hooks enforce formatting and linting before changes land in version control. ## Troubleshooting -- Ensure all dependencies in `environment.yml` are installed. -- Check dataset format and paths. -- Review Azure ML compute and environment configuration if running in the cloud. +- Verify `environment.yml` or `pyproject.toml` dependencies are installed. +- Ensure datasets follow the expected directory structure (`data/raw/audios/...`). +- For Azure ML issues, confirm workspace credentials and compute targets. + +## Fine-Tuning Tips +| Parameter | Recommended | Why | +| --- | --- | --- | +| `per_device_train_batch_size` | 4–8 | Small datasets benefit from smaller batches | +| `num_train_epochs` | 10–30 | Compensate for limited data with more passes | +| `warmup_steps` | 50–100 | Faster ramp-up than large fixed counts | +| `eval_strategy` | `epoch` | Evaluate once per epoch to avoid noise | +| `save_strategy` | `epoch` | Align checkpointing with evaluation | +| `load_best_model_at_end` | `True` | Automatically keep the best-performing checkpoint | + +Additional guidance on data augmentation, freezing layers, early stopping, and MLflow logging is summarized in `src/core/train/README.md`. ## License -This project is licensed under the MIT License. - -## Contact -For questions or support, please open an issue or contact the repository maintainer. - - -## Tips current scenario -| Parameter | Suggested | Reason | -|--------------------------------|------------------------|--------------------------------------------------------------------------------------------------| -| per_device_train_batch_size | 4–8 | With only 30 samples, large batches are unnecessary and may cause instability. | -| gradient_accumulation_steps | 1–4 | Increase if you want to simulate larger batches without memory overhead. | -| num_train_epochs | 10–30 | More epochs help the model learn from limited data. Use early stopping or monitor validation loss. | -| max_steps | Remove or set to -1 | Let training be driven by num_train_epochs to avoid premature stopping. | -| warmup_steps | 50–100 | 20k is too high for such a small dataset. | -| learning_rate | 1e-5 to 3e-5 | Your current value is reasonable, but monitor for overfitting. | -| eval_strategy | "epoch" | With few samples, evaluating per epoch is more meaningful. | -| save_strategy | "epoch" | Align with evaluation to save meaningful checkpoints. | -| fp16 | ✅ | Keep if your hardware supports it. | -| gradient_checkpointing | ✅ | Helps with memory efficiency. | -| optim | "adamw_bnb_8bit" | Good choice for memory-constrained environments. | -| load_best_model_at_end | ✅ | Helps select the best checkpoint. | -| predict_with_generate | ✅ | Needed for Whisper’s transcription tasks. | - ---- - -### 🧠 Additional Tips - -- **Use Data Augmentation**: Consider adding noise, pitch shift, or speed variation to artificially expand your dataset. -- **Freeze Most Layers**: Fine-tune only the final layers or adapters to reduce overfitting. -- **Use Early Stopping**: Monitor validation loss and stop training when it plateaus. -- **Log Carefully**: Avoid logging too many parameters to MLflow to prevent the `INVALID_PARAMETER_VALUE` error. +Licensed under the MIT License. Contributions and issues are welcome. diff --git a/apps/whisper_fine_tuning/src/core/data_prep/README.md b/apps/whisper_fine_tuning/src/core/data_prep/README.md index 306b82e..31ac02d 100644 --- a/apps/whisper_fine_tuning/src/core/data_prep/README.md +++ b/apps/whisper_fine_tuning/src/core/data_prep/README.md @@ -53,25 +53,25 @@ Run the data preparation pipeline from the project root to create domain-specifi **Training Data:** ```bash -python src/core/data_prep/data_prep.py \ - --source_data_dir data/raw/audios/matis \ - --output_data_dir data/raw/samples/training \ +python apps/whisper_fine_tuning/src/core/data_prep/main_data_prep.py \ + --source_data_dir apps/whisper_fine_tuning/data/raw/audios/matis \ + --output_data_dir apps/whisper_fine_tuning/data/raw/samples/training \ --domain train ``` **Testing Data:** ```bash -python src/core/data_prep/data_prep.py \ - --source_data_dir data/raw/audios/matis \ - --output_data_dir data/raw/samples/testing \ +python apps/whisper_fine_tuning/src/core/data_prep/main_data_prep.py \ + --source_data_dir apps/whisper_fine_tuning/data/raw/audios/matis \ + --output_data_dir apps/whisper_fine_tuning/data/raw/samples/testing \ --domain test ``` **Evaluation Data:** ```bash -python src/core/data_prep/data_prep.py \ - --source_data_dir data/raw/audios/matis \ - --output_data_dir data/raw/samples/evaluation \ +python apps/whisper_fine_tuning/src/core/data_prep/main_data_prep.py \ + --source_data_dir apps/whisper_fine_tuning/data/raw/audios/matis \ + --output_data_dir apps/whisper_fine_tuning/data/raw/samples/evaluation \ --domain evaluation ``` @@ -81,7 +81,7 @@ python src/core/data_prep/data_prep.py \ After preparing your datasets, run the complete pipeline locally: ```bash -python src/core/data_prep/main_silver_data_prep.py --train_datasets data/raw/samples/training --eval_datasets data/raw/samples/evaluation --test_datasets data/raw/samples/testing +python apps/whisper_fine_tuning/src/core/data_prep/main_silver_data_prep.py --train_datasets apps/whisper_fine_tuning/data/raw/samples/training --eval_datasets apps/whisper_fine_tuning/data/raw/samples/evaluation --test_datasets apps/whisper_fine_tuning/data/raw/samples/testing ``` This command will use the prepared datasets for the complete Whisper fine-tuning workflow. diff --git a/apps/whisper_fine_tuning/src/core/train/README.md b/apps/whisper_fine_tuning/src/core/train/README.md index 96313c2..ffa6bbb 100644 --- a/apps/whisper_fine_tuning/src/core/train/README.md +++ b/apps/whisper_fine_tuning/src/core/train/README.md @@ -30,14 +30,17 @@ core/train/ ``` 2. Launch training with the primary CLI: ```bash - python src/core/train/main_train.py \ + python apps/whisper_fine_tuning/src/core/train/main_train.py \ --model_name openai/whisper-large-v2 \ - --dataset data/silver/dataset \ + --dataset apps/whisper_fine_tuning/data/silver/dataset \ --output_dir output_model_dir \ --apply_lora True ``` 3. Monitor MLflow for run metadata, metrics, and artifacts. + +- All console and logging output will be saved as an MLflow artifact (`training_console.log`). + ## CLI Overview (`main_train.py`) - `--model_name`: Hugging Face identifier for the base Whisper model. - `--dataset`: Path to the on-disk Hugging Face dataset produced during data prep. From 097f30a5d7244d125da46b3e32526092e5105e2d Mon Sep 17 00:00:00 2001 From: Karina Assini Date: Mon, 3 Nov 2025 16:55:52 -0300 Subject: [PATCH 4/4] black fixes --- .../deployment/endpoint/inference.py | 19 ++- .../deployment/endpoint/terraform/score.py | 14 ++- .../deployment/register/register_model.py | 109 +++++++++--------- .../src/core/data_prep/main_data_prep.py | 2 +- .../src/core/evaluation/evaluation_process.py | 14 ++- .../src/core/train/main_train.py | 17 ++- .../src/core/train/main_train_nemo.py | 25 +++- .../src/core/train/train_nemo.py | 33 ++++-- 8 files changed, 138 insertions(+), 95 deletions(-) diff --git a/apps/whisper_fine_tuning/deployment/endpoint/inference.py b/apps/whisper_fine_tuning/deployment/endpoint/inference.py index 9f0817b..d502fcd 100644 --- a/apps/whisper_fine_tuning/deployment/endpoint/inference.py +++ b/apps/whisper_fine_tuning/deployment/endpoint/inference.py @@ -12,7 +12,9 @@ # https://docs.microsoft.com/azure/machine-learning/how-to-deploy-advanced-entry-script load_dotenv() # Load environment variables from a .env file -audio_path = pathlib.Path("/Users/karinaassiniandreatta/Documents/06 microsoft_codes/azuresamples/whisper-fine-tuning/src/core/create_data/matis/train_data/audio2.ogg") +audio_path = pathlib.Path( + "/Users/karinaassiniandreatta/Documents/06 microsoft_codes/azuresamples/whisper-fine-tuning/src/core/create_data/matis/train_data/audio2.ogg" +) audio_b64 = base64.b64encode(audio_path.read_bytes()).decode("utf-8") payload = { @@ -25,14 +27,21 @@ } body = str.encode(json.dumps(payload)) -url = os.getenv("SCORE_ENDPOINT", "https://ml-sandbox-core-hdydq.eastus2.inference.ml.azure.com/score") +url = os.getenv( + "SCORE_ENDPOINT", + "https://ml-sandbox-core-hdydq.eastus2.inference.ml.azure.com/score", +) # Replace this with the primary/secondary key, AMLToken, or Microsoft Entra ID token for the endpoint api_key = os.getenv("API_KEY") if not api_key: raise Exception("A key should be provided to invoke the endpoint") -headers = {'Content-Type':'application/json', 'Accept': 'application/json', 'Authorization':('Bearer '+ api_key)} +headers = { + "Content-Type": "application/json", + "Accept": "application/json", + "Authorization": ("Bearer " + api_key), +} req = urllib.request.Request(url, body, headers) @@ -58,6 +67,4 @@ # Print the headers - they include the requert ID and the timestamp, which are useful for debugging the failure print(error.info()) - print(error.read().decode("utf8", 'ignore')) - - + print(error.read().decode("utf8", "ignore")) diff --git a/apps/whisper_fine_tuning/deployment/endpoint/terraform/score.py b/apps/whisper_fine_tuning/deployment/endpoint/terraform/score.py index 4ce4a3a..8b64add 100644 --- a/apps/whisper_fine_tuning/deployment/endpoint/terraform/score.py +++ b/apps/whisper_fine_tuning/deployment/endpoint/terraform/score.py @@ -6,6 +6,7 @@ import mlflow import torch + def init(): global asr_pipeline, device, forced_decoder_ids @@ -24,14 +25,14 @@ def init(): asr_pipeline.model.to(device) asr_pipeline.model.eval() - forced_decoder_ids = asr_pipeline.tokenizer.get_decoder_prompt_ids( - language="portuguese", - task="transcribe", - ) + language="portuguese", + task="transcribe", + ) print("Whisper pipeline loaded via MLflow and ready.") + def run(raw_data): try: # Azure ML sends input_data as a DataFrame in split orientation @@ -59,13 +60,14 @@ def run(raw_data): import numpy as np import io import soundfile as sf + audio_array, _ = sf.read(io.BytesIO(audio_bytes), dtype="float32") generate_kwargs = { "repetition_penalty": 1.2, "no_repeat_ngram_size": 3, "max_new_tokens": 128, "do_sample": False, - "forced_decoder_ids": forced_decoder_ids + "forced_decoder_ids": forced_decoder_ids, } pipeline_inputs = {"array": audio_array, "sampling_rate": 16000} @@ -77,4 +79,4 @@ def run(raw_data): results.append({"transcription": transcription}) return json.dumps(results) except Exception as e: - return json.dumps({"error": str(e)}) \ No newline at end of file + return json.dumps({"error": str(e)}) diff --git a/apps/whisper_fine_tuning/deployment/register/register_model.py b/apps/whisper_fine_tuning/deployment/register/register_model.py index 9a581cb..764915a 100644 --- a/apps/whisper_fine_tuning/deployment/register/register_model.py +++ b/apps/whisper_fine_tuning/deployment/register/register_model.py @@ -19,39 +19,39 @@ def get_latest_run_from_experiment(experiment_name: str) -> str: """ Get the latest run ID from the specified experiment. - + Args: experiment_name: Name of the MLflow experiment - + Returns: run_id: The ID of the latest run """ client = MlflowClient() - + try: experiment = client.get_experiment_by_name(experiment_name) if experiment is None: raise ValueError(f"Experiment '{experiment_name}' not found") - + experiment_id = experiment.experiment_id - + # Get runs from experiment, sorted by start time (latest first) runs = client.search_runs( - experiment_ids=[experiment_id], - order_by=["start_time DESC"], - max_results=1 + experiment_ids=[experiment_id], order_by=["start_time DESC"], max_results=1 ) - + if not runs: raise ValueError(f"No runs found in experiment '{experiment_name}'") - + latest_run = runs[0] logger.info(f"Found latest run: {latest_run.info.run_id}") logger.info(f"Run status: {latest_run.info.status}") - logger.info(f"Run start time: {datetime.fromtimestamp(latest_run.info.start_time / 1000)}") - + logger.info( + f"Run start time: {datetime.fromtimestamp(latest_run.info.start_time / 1000)}" + ) + return latest_run.info.run_id - + except Exception as e: logger.error(f"Error getting latest run: {e}") raise @@ -60,34 +60,31 @@ def get_latest_run_from_experiment(experiment_name: str) -> str: def register_model(run_id: str, model_name: str, artifact_path: str = "model") -> str: """ Register a model from the specified run. - + Args: run_id: MLflow run ID containing the model model_name: Name to register the model under artifact_path: Path to the model artifact in the run - + Returns: model_version: The version number of the registered model """ client = MlflowClient() - + try: # Construct the model URI model_uri = f"runs:/{run_id}/{artifact_path}" - + # Register the model - model_version = mlflow.register_model( - model_uri=model_uri, - name=model_name - ) - + model_version = mlflow.register_model(model_uri=model_uri, name=model_name) + logger.info(f"Model registered successfully!") logger.info(f"Model name: {model_name}") logger.info(f"Model version: {model_version.version}") logger.info(f"Model URI: {model_uri}") - + return model_version.version - + except Exception as e: logger.error(f"Error registering model: {e}") raise @@ -96,23 +93,21 @@ def register_model(run_id: str, model_name: str, artifact_path: str = "model") - def set_model_stage(model_name: str, version: str, stage: str = "Staging"): """ Set the stage for a model version. - + Args: model_name: Name of the registered model version: Version number of the model stage: Stage to set (None, Staging, Production, Archived) """ client = MlflowClient() - + try: client.transition_model_version_stage( - name=model_name, - version=version, - stage=stage + name=model_name, version=version, stage=stage ) - + logger.info(f"Model {model_name} version {version} moved to {stage} stage") - + except Exception as e: logger.error(f"Error setting model stage: {e}") raise @@ -122,44 +117,44 @@ def main(): parser = argparse.ArgumentParser( description="Register the latest model from an MLflow experiment" ) - + parser.add_argument( "--experiment_name", type=str, required=True, - help="Name of the MLflow experiment to get the latest run from" + help="Name of the MLflow experiment to get the latest run from", ) - + parser.add_argument( "--model_name", type=str, default=None, - help="Name to register the model under. If not provided, will be auto-generated from experiment name" + help="Name to register the model under. If not provided, will be auto-generated from experiment name", ) - + parser.add_argument( "--artifact_path", type=str, default="model", - help="Path to the model artifact in the run (default: 'model')" + help="Path to the model artifact in the run (default: 'model')", ) - + parser.add_argument( "--stage", type=str, default="Staging", choices=["None", "Staging", "Production", "Archived"], - help="Stage to set for the registered model (default: 'Staging')" + help="Stage to set for the registered model (default: 'Staging')", ) - + parser.add_argument( "--also_register_adapter", action="store_true", - help="Also register the PEFT adapter if available" + help="Also register the PEFT adapter if available", ) - + args = parser.parse_args() - + # Generate model name if not provided if args.model_name is None: # Extract a clean model name from experiment name @@ -169,43 +164,47 @@ def main(): args.model_name = f"whisper-{experiment_parts[1]}-{experiment_parts[3]}-{experiment_parts[4]}" else: args.model_name = args.experiment_name.replace("-", "_") - + logger.info(f"Auto-generated model name: {args.model_name}") - + try: # Get the latest run from the experiment logger.info(f"Getting latest run from experiment: {args.experiment_name}") run_id = get_latest_run_from_experiment(args.experiment_name) - + # Register the main model logger.info(f"Registering model: {args.model_name}") version = register_model(run_id, args.model_name, args.artifact_path) - + # Set the stage if args.stage != "None": logger.info(f"Setting model stage to: {args.stage}") set_model_stage(args.model_name, version, args.stage) - + # Also register adapter if requested if args.also_register_adapter: try: adapter_model_name = f"{args.model_name}-adapter" logger.info(f"Registering adapter model: {adapter_model_name}") - adapter_version = register_model(run_id, adapter_model_name, "adapter_peft") - + adapter_version = register_model( + run_id, adapter_model_name, "adapter_peft" + ) + if args.stage != "None": set_model_stage(adapter_model_name, adapter_version, args.stage) - + except Exception as e: logger.warning(f"Could not register adapter model: {e}") - + logger.info("Model registration completed successfully!") - logger.info(f"You can now use the model with: mlflow.transformers.load_model('models:/{args.model_name}/{version}')") - + logger.info( + f"You can now use the model with: mlflow.transformers.load_model('models:/{args.model_name}/{version}')" + ) + except Exception as e: logger.error(f"Model registration failed: {e}") sys.exit(1) if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/apps/whisper_fine_tuning/src/core/data_prep/main_data_prep.py b/apps/whisper_fine_tuning/src/core/data_prep/main_data_prep.py index 612e89f..c4c758e 100644 --- a/apps/whisper_fine_tuning/src/core/data_prep/main_data_prep.py +++ b/apps/whisper_fine_tuning/src/core/data_prep/main_data_prep.py @@ -169,4 +169,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/apps/whisper_fine_tuning/src/core/evaluation/evaluation_process.py b/apps/whisper_fine_tuning/src/core/evaluation/evaluation_process.py index b3b0787..a13cee6 100644 --- a/apps/whisper_fine_tuning/src/core/evaluation/evaluation_process.py +++ b/apps/whisper_fine_tuning/src/core/evaluation/evaluation_process.py @@ -89,13 +89,15 @@ def main(args): logging.info("Custom model and tokenizer loaded successfully.") whisper_asr = pipeline( - "automatic-speech-recognition", model=model_id, device=args.device, + "automatic-speech-recognition", + model=model_id, + device=args.device, generate_kwargs={ - "repetition_penalty": 1.2, - "no_repeat_ngram_size": 3, - "max_new_tokens": 128, - "do_sample": False - } + "repetition_penalty": 1.2, + "no_repeat_ngram_size": 3, + "max_new_tokens": 128, + "do_sample": False, + }, ) whisper_asr.model.config.forced_decoder_ids = ( diff --git a/apps/whisper_fine_tuning/src/core/train/main_train.py b/apps/whisper_fine_tuning/src/core/train/main_train.py index 4c4a9cd..f5f0317 100644 --- a/apps/whisper_fine_tuning/src/core/train/main_train.py +++ b/apps/whisper_fine_tuning/src/core/train/main_train.py @@ -17,7 +17,10 @@ # Fix the path to point to the repository root where 'src' directory is located from pathlib import Path -APP_ROOT = Path(__file__).resolve().parents[3] # …/language-creation/apps/whisper_fine_tuning + +APP_ROOT = ( + Path(__file__).resolve().parents[3] +) # …/language-creation/apps/whisper_fine_tuning if str(APP_ROOT) not in sys.path: sys.path.insert(0, str(APP_ROOT)) print(sys.path) @@ -89,7 +92,7 @@ def main(): default="steps", help="list of datasets to be used for training.", ) - + # Add experiment name argument parser.add_argument( "--experiment_name", @@ -105,11 +108,13 @@ def main(): # Generate experiment name if not provided if args.experiment_name is None: - model_short = args.model_name.split('/')[-1] # e.g., "whisper-large-v2" + model_short = args.model_name.split("/")[-1] # e.g., "whisper-large-v2" language_short = args.language.lower() lora_suffix = "-lora" if args.apply_lora else "" - timestamp = datetime.now().strftime('%Y%m%d') - args.experiment_name = f"whisper-{language_short}-{model_short}{lora_suffix}-{timestamp}" + timestamp = datetime.now().strftime("%Y%m%d") + args.experiment_name = ( + f"whisper-{language_short}-{model_short}{lora_suffix}-{timestamp}" + ) # Set MLflow experiment mlflow.set_experiment(args.experiment_name) @@ -199,4 +204,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/apps/whisper_fine_tuning/src/core/train/main_train_nemo.py b/apps/whisper_fine_tuning/src/core/train/main_train_nemo.py index 3df4741..058376c 100644 --- a/apps/whisper_fine_tuning/src/core/train/main_train_nemo.py +++ b/apps/whisper_fine_tuning/src/core/train/main_train_nemo.py @@ -1,5 +1,5 @@ - import os + os.environ.setdefault("LIGHTNING_DISABLE_ADLFS_IMPORT", "1") os.environ.setdefault("NUMBA_CUDA_ARCH_LIST", "70") # use "75" if you prefer os.environ.setdefault("NUMBA_CUDA_USE_NVIDIA_BINDING", "1") @@ -15,6 +15,7 @@ import mlflow import pytorch_lightning as pl import torch + if torch.cuda.is_available(): torch.set_float32_matmul_precision("high") torch.backends.cuda.matmul.allow_tf32 = True @@ -36,7 +37,10 @@ logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) -import faulthandler; faulthandler.enable() +import faulthandler + +faulthandler.enable() + def resolve_checkpoint_path( checkpoint_path: Optional[str], @@ -85,7 +89,9 @@ def main() -> None: parser.add_argument("--num_workers", type=int, default=4) parser.add_argument("--sample_rate", type=int, default=16000) - parser.add_argument("--apply_lora", dest="apply_lora", action="store_true", default=True) + parser.add_argument( + "--apply_lora", dest="apply_lora", action="store_true", default=True + ) parser.add_argument("--no_lora", dest="apply_lora", action="store_false") parser.add_argument("--lora_r", type=int, default=64) parser.add_argument("--lora_alpha", type=int, default=128) @@ -132,7 +138,10 @@ def main() -> None: logger.info("CUDA available: %s", gpu_available) if gpu_available: logger.info("CUDA devices: %d", torch.cuda.device_count()) - logger.info("Current device: %s", torch.cuda.get_device_name(torch.cuda.current_device())) + logger.info( + "Current device: %s", + torch.cuda.get_device_name(torch.cuda.current_device()), + ) else: logger.warning("GPU not detected; training will fall back to CPU.") @@ -180,7 +189,11 @@ def main() -> None: ) target_modules = ( - [module.strip() for module in args.lora_target_modules.split(",") if module.strip()] + [ + module.strip() + for module in args.lora_target_modules.split(",") + if module.strip() + ] if args.lora_target_modules else None ) @@ -216,4 +229,4 @@ def main() -> None: if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/apps/whisper_fine_tuning/src/core/train/train_nemo.py b/apps/whisper_fine_tuning/src/core/train/train_nemo.py index 8ff6034..63a298d 100644 --- a/apps/whisper_fine_tuning/src/core/train/train_nemo.py +++ b/apps/whisper_fine_tuning/src/core/train/train_nemo.py @@ -16,7 +16,9 @@ logger = logging.getLogger(__name__) try: - from nemo.collections.common.parts.mixins.adapter_mixins import LoraConfig as NemoLoraConfig + from nemo.collections.common.parts.mixins.adapter_mixins import ( + LoraConfig as NemoLoraConfig, + ) except ImportError: NemoLoraConfig = None @@ -65,6 +67,7 @@ class RNNTLoraConfig: use_dora: bool = False adapter_name: str = "rnnt_lora" + def create_manifest_from_dataset( dataset: Dataset, manifest_path: str, @@ -82,7 +85,9 @@ def create_manifest_from_dataset( for row in dataset: audio_path = row["audio_path"] if not audio_path: - raise ValueError("Each row must include an audio path for NeMo manifests.") + raise ValueError( + "Each row must include an audio path for NeMo manifests." + ) duration = None # Prefer explicit columns produced during data prep @@ -116,6 +121,7 @@ def create_manifest_from_dataset( logger.info("Wrote %d rows to %s", len(dataset), manifest_file) return str(manifest_file.resolve()) + class NemoRNNTTrainer: """Fine-tune a NeMo RNNT model with optional LoRA and MLflow logging.""" @@ -156,7 +162,9 @@ def train( mlflow.set_experiment(experiment_name) trainer = pl.Trainer(**self._build_trainer_kwargs()) - model = EncDecRNNTBPEModel.restore_from(self.base_checkpoint_path, map_location=self.device) + model = EncDecRNNTBPEModel.restore_from( + self.base_checkpoint_path, map_location=self.device + ) model.to(self.device) self._configure_model(model, train_manifest, val_manifest) @@ -219,7 +227,6 @@ def _log_params(self) -> None: if self.apply_lora: self.log_params_from_dict(asdict(self.lora_config), parent_key="lora") - def _configure_model( self, model: EncDecRNNTBPEModel, @@ -282,7 +289,9 @@ def _maybe_enable_lora(self, model: EncDecRNNTBPEModel) -> None: if not self.apply_lora: return if NemoLoraConfig is None or not hasattr(model, "enable_lora"): - logger.warning("LoRA support is unavailable in this NeMo build; continuing without adapters.") + logger.warning( + "LoRA support is unavailable in this NeMo build; continuing without adapters." + ) self.apply_lora = False return @@ -298,7 +307,9 @@ def _maybe_enable_lora(self, model: EncDecRNNTBPEModel) -> None: nemo_lora_cfg = NemoLoraConfig(**lora_kwargs) try: - model.enable_lora(config=nemo_lora_cfg, adapter_name=self.lora_config.adapter_name) + model.enable_lora( + config=nemo_lora_cfg, adapter_name=self.lora_config.adapter_name + ) except TypeError: model.enable_lora(nemo_lora_cfg) self._adapter_name = self.lora_config.adapter_name @@ -363,7 +374,9 @@ def log_params_from_dict(config: Dict[str, Any], parent_key: str = "") -> None: mlflow.log_param(full_key, str(value)[:max_length]) def _default_run_name(self) -> str: - return f"{self.model_name}-finetune-{datetime.utcnow().strftime('%Y%m%d-%H%M%S')}" + return ( + f"{self.model_name}-finetune-{datetime.utcnow().strftime('%Y%m%d-%H%M%S')}" + ) def train_nemo_from_datasets( @@ -387,7 +400,9 @@ def train_nemo_from_datasets( """Convenience wrapper to fine-tune NeMo RNNT models from Hugging Face datasets.""" if val_dataset is None: if not 0 < validation_split < 1: - raise ValueError("validation_split must be between 0 and 1 when no validation dataset is provided.") + raise ValueError( + "validation_split must be between 0 and 1 when no validation dataset is provided." + ) split = train_dataset.train_test_split(test_size=validation_split, seed=42) train_dataset, val_dataset = split["train"], split["test"] @@ -428,4 +443,4 @@ def train_nemo_from_datasets( val_manifest=str(val_manifest), experiment_name=experiment_name, run_name=run_name, - ) \ No newline at end of file + )

          Copy a token from your Hugging Face\ntokens page           and paste it below.
          Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.

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