clean-up eval scripts

SWivid · SWivid · commit 529d8561331a · 2025-10-27T14:38:57.000Z
diff --git a/src/f5_tts/eval/README.md b/src/f5_tts/eval/README.md
@@ -14,16 +14,20 @@ pip install -e .[eval]
 1. *Seed-TTS testset*: Download from [seed-tts-eval](https://github.com/BytedanceSpeech/seed-tts-eval).
 2. *LibriSpeech test-clean*: Download from [OpenSLR](http://www.openslr.org/12/).
 3. Unzip the downloaded datasets and place them in the `data/` directory.
-4. Update the path for *LibriSpeech test-clean* data in `src/f5_tts/eval/eval_infer_batch.py`
-5. Our filtered LibriSpeech-PC 4-10s subset: `data/librispeech_pc_test_clean_cross_sentence.lst`
+4. Our filtered LibriSpeech-PC 4-10s subset: `data/librispeech_pc_test_clean_cross_sentence.lst`
 
 ### Batch Inference for Test Set
 
 To run batch inference for evaluations, execute the following commands:
 
 ```bash
-# batch inference for evaluations
-accelerate config  # if not set before
+# if not setup accelerate config yet
+accelerate config
+
+# if only perform inference
+bash src/f5_tts/eval/eval_infer_batch.sh --infer-only
+
+# if inference and with corresponding evaluation, setup the following tools first
 bash src/f5_tts/eval/eval_infer_batch.sh
 ```
 
@@ -35,9 +39,13 @@ bash src/f5_tts/eval/eval_infer_batch.sh
 2. English ASR Model: [Faster-Whisper](https://huggingface.co/Systran/faster-whisper-large-v3)
 3. WavLM Model: Download from [Google Drive](https://drive.google.com/file/d/1-aE1NfzpRCLxA4GUxX9ITI3F9LlbtEGP/view).
 
-Then update in the following scripts with the paths you put evaluation model ckpts to.
+> [!NOTE]  
+> ASR model will be automatically downloaded if `--local` not set for evaluation scripts.  
+> Otherwise, you should update the `asr_ckpt_dir` path values in `eval_librispeech_test_clean.py` or `eval_seedtts_testset.py`.
+> 
+> WavLM model must be downloaded and your `wavlm_ckpt_dir` path updated in `eval_librispeech_test_clean.py` and `eval_seedtts_testset.py`.
 
-### Objective Evaluation
+### Objective Evaluation Examples
 
 Update the path with your batch-inferenced results, and carry out WER / SIM / UTMOS evaluations:
 ```bash
@@ -50,3 +58,6 @@ python src/f5_tts/eval/eval_librispeech_test_clean.py --eval_task sim --gen_wav_
 # Evaluation [UTMOS]. --ext: Audio extension
 python src/f5_tts/eval/eval_utmos.py --audio_dir <WAV_DIR> --ext wav
 ```
+
+> [!NOTE]  
+> Evaluation results can also be found in `_*_results.jsonl` files saved in `<GEN_WAV_DIR>`/`<WAV_DIR>`.
diff --git a/src/f5_tts/eval/eval_infer_batch.py b/src/f5_tts/eval/eval_infer_batch.py
@@ -48,6 +48,11 @@ def main():
     parser.add_argument("-ss", "--swaysampling", default=-1, type=float)
 
     parser.add_argument("-t", "--testset", required=True)
+    parser.add_argument(
+        "-p", "--librispeech_test_clean_path", default=f"{rel_path}/data/LibriSpeech/test-clean", type=str
+    )
+
+    parser.add_argument("--local", action="store_true", help="Use local vocoder checkpoint directory")
 
     args = parser.parse_args()
 
@@ -83,7 +88,7 @@ def main():
 
     if testset == "ls_pc_test_clean":
         metalst = rel_path + "/data/librispeech_pc_test_clean_cross_sentence.lst"
-        librispeech_test_clean_path = "<SOME_PATH>/LibriSpeech/test-clean"  # test-clean path
+        librispeech_test_clean_path = args.librispeech_test_clean_path
         metainfo = get_librispeech_test_clean_metainfo(metalst, librispeech_test_clean_path)
 
     elif testset == "seedtts_test_zh":
@@ -121,7 +126,7 @@ def main():
     )
 
     # Vocoder model
-    local = False
+    local = args.local
     if mel_spec_type == "vocos":
         vocoder_local_path = "../checkpoints/charactr/vocos-mel-24khz"
     elif mel_spec_type == "bigvgan":
diff --git a/src/f5_tts/eval/eval_infer_batch.sh b/src/f5_tts/eval/eval_infer_batch.sh
@@ -1,18 +1,116 @@
 #!/bin/bash
+set -e
+export PYTHONWARNINGS="ignore::UserWarning,ignore::FutureWarning"
 
-# e.g. F5-TTS, 16 NFE
-accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_v1_Base" -t "seedtts_test_zh" -nfe 16
-accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_v1_Base" -t "seedtts_test_en" -nfe 16
-accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_v1_Base" -t "ls_pc_test_clean" -nfe 16
+# Configuration parameters
+MODEL_NAME="F5TTS_v1_Base"
+SEEDS=(0 1 2)
+CKPTSTEPS=(1250000)
+TASKS=("seedtts_test_zh" "seedtts_test_en" "ls_pc_test_clean")
+LS_TEST_CLEAN_PATH="data/LibriSpeech/test-clean"
+GPUS="[0,1,2,3,4,5,6,7]"
+OFFLINE_MODE=false
 
-# e.g. Vanilla E2 TTS, 32 NFE
-accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -c 1200000 -t "seedtts_test_zh" -o "midpoint" -ss 0
-accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -c 1200000 -t "seedtts_test_en" -o "midpoint" -ss 0
-accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -c 1200000 -t "ls_pc_test_clean" -o "midpoint" -ss 0
+# Parse arguments
+if [ $OFFLINE_MODE = true ]; then
+    LOCAL="--local"
+else
+    LOCAL=""
+fi
+INFER_ONLY=false
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --infer-only)
+            INFER_ONLY=true
+            shift
+            ;;
+        *)
+            echo "======== Unknown parameter: $1"
+            exit 1
+            ;;
+    esac
+done
 
-# e.g. evaluate F5-TTS 16 NFE result on Seed-TTS test-zh
-python src/f5_tts/eval/eval_seedtts_testset.py -e wer -l zh --gen_wav_dir results/F5TTS_v1_Base_1250000/seedtts_test_zh/seed0_euler_nfe16_vocos_ss-1_cfg2.0_speed1.0 --gpu_nums 8
-python src/f5_tts/eval/eval_seedtts_testset.py -e sim -l zh --gen_wav_dir results/F5TTS_v1_Base_1250000/seedtts_test_zh/seed0_euler_nfe16_vocos_ss-1_cfg2.0_speed1.0 --gpu_nums 8
-python src/f5_tts/eval/eval_utmos.py --audio_dir results/F5TTS_v1_Base_1250000/seedtts_test_zh/seed0_euler_nfe16_vocos_ss-1_cfg2.0_speed1.0
+echo "======== Starting F5-TTS batch evaluation task..."
+if [ "$INFER_ONLY" = true ]; then
+    echo "======== Mode: Execute infer tasks only"
+else
+    echo "======== Mode: Execute full pipeline (infer + eval)"
+fi
 
-# etc.
+# Function: Execute eval tasks
+execute_eval_tasks() {
+    local ckptstep=$1
+    local seed=$2
+    local task_name=$3
+    
+    local gen_wav_dir="results/${MODEL_NAME}_${ckptstep}/${task_name}/seed${seed}_euler_nfe32_vocos_ss-1_cfg2.0_speed1.0"
+    
+    echo ">>>>>>>> Starting eval task: ckptstep=${ckptstep}, seed=${seed}, task=${task_name}"
+    
+    case $task_name in
+        "seedtts_test_zh")
+            python src/f5_tts/eval/eval_seedtts_testset.py -e wer -l zh -g "$gen_wav_dir" -n "$GPUS" $LOCAL
+            python src/f5_tts/eval/eval_seedtts_testset.py -e sim -l zh -g "$gen_wav_dir" -n "$GPUS" $LOCAL
+            python src/f5_tts/eval/eval_utmos.py --audio_dir "$gen_wav_dir"
+            ;;
+        "seedtts_test_en")
+            python src/f5_tts/eval/eval_seedtts_testset.py -e wer -l en -g "$gen_wav_dir" -n "$GPUS" $LOCAL
+            python src/f5_tts/eval/eval_seedtts_testset.py -e sim -l en -g "$gen_wav_dir" -n "$GPUS" $LOCAL
+            python src/f5_tts/eval/eval_utmos.py --audio_dir "$gen_wav_dir"
+            ;;
+        "ls_pc_test_clean")
+            python src/f5_tts/eval/eval_librispeech_test_clean.py -e wer -g "$gen_wav_dir" -n "$GPUS" -p "$LS_TEST_CLEAN_PATH" $LOCAL
+            python src/f5_tts/eval/eval_librispeech_test_clean.py -e sim -g "$gen_wav_dir" -n "$GPUS" -p "$LS_TEST_CLEAN_PATH" $LOCAL
+            python src/f5_tts/eval/eval_utmos.py --audio_dir "$gen_wav_dir"
+            ;;
+    esac
+    
+    echo ">>>>>>>> Completed eval task: ckptstep=${ckptstep}, seed=${seed}, task=${task_name}"
+}
+
+# Main execution loop
+for ckptstep in "${CKPTSTEPS[@]}"; do
+    echo "======== Processing ckptstep: ${ckptstep}"
+    
+    for seed in "${SEEDS[@]}"; do
+        echo "-------- Processing seed: ${seed}"
+        
+        # Store eval task PIDs for current seed (if not infer-only mode)
+        if [ "$INFER_ONLY" = false ]; then
+            declare -a eval_pids
+        fi
+        
+        # Execute each infer task sequentially
+        for task in "${TASKS[@]}"; do
+            echo ">>>>>>>> Executing infer task: accelerate launch src/f5_tts/eval/eval_infer_batch.py -s ${seed} -n \"${MODEL_NAME}\" -t \"${task}\" -c ${ckptstep} $LOCAL"
+            
+            # Execute infer task (foreground execution, wait for completion)
+            accelerate launch src/f5_tts/eval/eval_infer_batch.py -s ${seed} -n "${MODEL_NAME}" -t "${task}" -c ${ckptstep} -p "${LS_TEST_CLEAN_PATH}" $LOCAL
+            
+            # If not infer-only mode, launch corresponding eval task
+            if [ "$INFER_ONLY" = false ]; then
+                # Launch corresponding eval task (background execution, non-blocking for next infer)
+                execute_eval_tasks $ckptstep $seed $task &
+                eval_pids+=($!)
+            fi
+        done
+        
+        # If not infer-only mode, wait for all eval tasks of current seed to complete
+        if [ "$INFER_ONLY" = false ]; then
+            echo ">>>>>>>> All infer tasks for seed ${seed} completed, waiting for corresponding eval tasks to finish..."
+            
+            for pid in "${eval_pids[@]}"; do
+                wait $pid
+            done
+            
+            unset eval_pids  # Clean up array
+        fi
+        echo "-------- All eval tasks for seed ${seed} completed"
+    done
+    
+    echo "======== Completed ckptstep: ${ckptstep}"
+    echo
+done
+
+echo "======== All tasks completed!"
diff --git a/src/f5_tts/eval/eval_infer_batch_example.sh b/src/f5_tts/eval/eval_infer_batch_example.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+# e.g. F5-TTS, 16 NFE
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_v1_Base" -t "seedtts_test_zh" -nfe 16
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_v1_Base" -t "seedtts_test_en" -nfe 16
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_v1_Base" -t "ls_pc_test_clean" -nfe 16 -p data/LibriSpeech/test-clean
+
+# e.g. Vanilla E2 TTS, 32 NFE
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -c 1200000 -t "seedtts_test_zh" -o "midpoint" -ss 0
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -c 1200000 -t "seedtts_test_en" -o "midpoint" -ss 0
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -c 1200000 -t "ls_pc_test_clean" -o "midpoint" -ss 0 -p data/LibriSpeech/test-clean
+
+# e.g. evaluate F5-TTS 16 NFE result on Seed-TTS test-zh
+python src/f5_tts/eval/eval_seedtts_testset.py -e wer -l zh --gen_wav_dir results/F5TTS_v1_Base_1250000/seedtts_test_zh/seed0_euler_nfe16_vocos_ss-1_cfg2.0_speed1.0 --gpu_nums 8
+python src/f5_tts/eval/eval_seedtts_testset.py -e sim -l zh --gen_wav_dir results/F5TTS_v1_Base_1250000/seedtts_test_zh/seed0_euler_nfe16_vocos_ss-1_cfg2.0_speed1.0 --gpu_nums 8
+python src/f5_tts/eval/eval_utmos.py --audio_dir results/F5TTS_v1_Base_1250000/seedtts_test_zh/seed0_euler_nfe16_vocos_ss-1_cfg2.0_speed1.0
+
+# etc.
diff --git a/src/f5_tts/eval/eval_librispeech_test_clean.py b/src/f5_tts/eval/eval_librispeech_test_clean.py
@@ -1,6 +1,7 @@
 # Evaluate with Librispeech test-clean, ~3s prompt to generate 4-10s audio (the way of valle/voicebox evaluation)
 
 import argparse
+import ast
 import json
 import os
 import sys
@@ -25,11 +26,26 @@ def get_args():
     parser.add_argument("-l", "--lang", type=str, default="en")
     parser.add_argument("-g", "--gen_wav_dir", type=str, required=True)
     parser.add_argument("-p", "--librispeech_test_clean_path", type=str, required=True)
-    parser.add_argument("-n", "--gpu_nums", type=int, default=8, help="Number of GPUs to use")
+    parser.add_argument(
+        "-n", "--gpu_nums", type=str, default="8", help="Number of GPUs to use (e.g., 8) or GPU list (e.g., [0,1,2,3])"
+    )
     parser.add_argument("--local", action="store_true", help="Use local custom checkpoint directory")
     return parser.parse_args()
 
 
+def parse_gpu_nums(gpu_nums_str):
+    try:
+        if gpu_nums_str.startswith("[") and gpu_nums_str.endswith("]"):
+            gpu_list = ast.literal_eval(gpu_nums_str)
+            if isinstance(gpu_list, list):
+                return gpu_list
+        return list(range(int(gpu_nums_str)))
+    except (ValueError, SyntaxError):
+        raise argparse.ArgumentTypeError(
+            f"Invalid GPU specification: {gpu_nums_str}. Use a number (e.g., 8) or a list (e.g., [0,1,2,3])"
+        )
+
+
 def main():
     args = get_args()
     eval_task = args.eval_task
@@ -38,7 +54,7 @@ def main():
     gen_wav_dir = args.gen_wav_dir
     metalst = rel_path + "/data/librispeech_pc_test_clean_cross_sentence.lst"
 
-    gpus = list(range(args.gpu_nums))
+    gpus = parse_gpu_nums(args.gpu_nums)
     test_set = get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path)
 
     ## In LibriSpeech, some speakers utilized varying voice characteristics for different characters in the book,
diff --git a/src/f5_tts/eval/eval_seedtts_testset.py b/src/f5_tts/eval/eval_seedtts_testset.py
@@ -1,6 +1,7 @@
 # Evaluate with Seed-TTS testset
 
 import argparse
+import ast
 import json
 import os
 import sys
@@ -24,11 +25,26 @@ def get_args():
     parser.add_argument("-e", "--eval_task", type=str, default="wer", choices=["sim", "wer"])
     parser.add_argument("-l", "--lang", type=str, default="en", choices=["zh", "en"])
     parser.add_argument("-g", "--gen_wav_dir", type=str, required=True)
-    parser.add_argument("-n", "--gpu_nums", type=int, default=8, help="Number of GPUs to use")
+    parser.add_argument(
+        "-n", "--gpu_nums", type=str, default="8", help="Number of GPUs to use (e.g., 8) or GPU list (e.g., [0,1,2,3])"
+    )
     parser.add_argument("--local", action="store_true", help="Use local custom checkpoint directory")
     return parser.parse_args()
 
 
+def parse_gpu_nums(gpu_nums_str):
+    try:
+        if gpu_nums_str.startswith("[") and gpu_nums_str.endswith("]"):
+            gpu_list = ast.literal_eval(gpu_nums_str)
+            if isinstance(gpu_list, list):
+                return gpu_list
+        return list(range(int(gpu_nums_str)))
+    except (ValueError, SyntaxError):
+        raise argparse.ArgumentTypeError(
+            f"Invalid GPU specification: {gpu_nums_str}. Use a number (e.g., 8) or a list (e.g., [0,1,2,3])"
+        )
+
+
 def main():
     args = get_args()
     eval_task = args.eval_task
@@ -38,7 +54,7 @@ def main():
 
     # NOTE. paraformer-zh result will be slightly different according to the number of gpus, cuz batchsize is different
     #       zh 1.254 seems a result of 4 workers wer_seed_tts
-    gpus = list(range(args.gpu_nums))
+    gpus = parse_gpu_nums(args.gpu_nums)
     test_set = get_seed_tts_test(metalst, gen_wav_dir, gpus)
 
     local = args.local
diff --git a/src/f5_tts/eval/utils_eval.py b/src/f5_tts/eval/utils_eval.py
@@ -395,14 +395,21 @@ def run_sim(args):
         wav1, sr1 = torchaudio.load(gen_wav)
         wav2, sr2 = torchaudio.load(prompt_wav)
 
-        resample1 = torchaudio.transforms.Resample(orig_freq=sr1, new_freq=16000)
-        resample2 = torchaudio.transforms.Resample(orig_freq=sr2, new_freq=16000)
-        wav1 = resample1(wav1)
-        wav2 = resample2(wav2)
-
         if use_gpu:
             wav1 = wav1.cuda(device)
             wav2 = wav2.cuda(device)
+
+        if sr1 != 16000:
+            resample1 = torchaudio.transforms.Resample(orig_freq=sr1, new_freq=16000)
+            if use_gpu:
+                resample1 = resample1.cuda(device)
+            wav1 = resample1(wav1)
+        if sr2 != 16000:
+            resample2 = torchaudio.transforms.Resample(orig_freq=sr2, new_freq=16000)
+            if use_gpu:
+                resample2 = resample2.cuda(device)
+            wav2 = resample2(wav2)
+
         with torch.no_grad():
             emb1 = model(wav1)
             emb2 = model(wav2)
diff --git a/src/f5_tts/scripts/count_max_epoch_precise.py b/src/f5_tts/scripts/count_max_epoch_precise.py
@@ -0,0 +1,32 @@
+import math
+
+from torch.utils.data import SequentialSampler
+
+from f5_tts.model.dataset import DynamicBatchSampler, load_dataset
+
+
+train_dataset = load_dataset("Emilia_ZH_EN", "pinyin")
+sampler = SequentialSampler(train_dataset)
+
+gpus = 8
+batch_size_per_gpu = 38400
+max_samples_per_gpu = 64
+max_updates = 1250000
+
+batch_sampler = DynamicBatchSampler(
+    sampler,
+    batch_size_per_gpu,
+    max_samples=max_samples_per_gpu,
+    random_seed=666,
+    drop_residual=False,
+)
+
+print(
+    f"One epoch has {len(batch_sampler) / gpus} updates if gpus={gpus}, with "
+    f"batch_size_per_gpu={batch_size_per_gpu} (frames) & "
+    f"max_samples_per_gpu={max_samples_per_gpu}."
+)
+print(
+    f"If gpus={gpus}, for max_updates={max_updates} "
+    f"should set epoch={math.ceil(max_updates / len(batch_sampler) * gpus)}."
+)
