make style

Author: ylacombe

parler_tts/configuration_parler_tts.py

@@ -40,7 +40,7 @@ class ParlerTTSDecoderConfig(PretrainedConfig):
     Args:
         vocab_size (`int`, *optional*, defaults to 2049):
             Vocabulary size of the ParlerTTSDecoder model. Defines the number of different tokens that can be
-            represented by the `inputs_ids` passed when calling [`ParlerTTSDecoder`]. 
+            represented by the `inputs_ids` passed when calling [`ParlerTTSDecoder`].
         hidden_size (`int`, *optional*, defaults to 1024):
             Dimensionality of the layers and the pooler layer.
         num_hidden_layers (`int`, *optional*, defaults to 24):

parler_tts/modeling_parler_tts.py

@@ -1522,7 +1522,7 @@ def generate(
             output_ids = outputs.sequences
         else:
             output_ids = outputs
-            
+
         # apply the pattern mask to the final ids
         output_ids = self.apply_delay_pattern_mask(output_ids, model_kwargs["delay_pattern_mask"])
 
@@ -2460,7 +2460,10 @@ def generate(
         if "encoder_outputs" not in model_kwargs:
             # encoder_outputs are created and added to `model_kwargs`
             model_kwargs = self._prepare_text_encoder_kwargs_for_generation(
-                inputs_tensor, model_kwargs, model_input_name, generation_config,
+                inputs_tensor,
+                model_kwargs,
+                model_input_name,
+                generation_config,
             )
 
         if "prompt_hidden_states" not in model_kwargs and "prompt_input_ids" in model_kwargs:
@@ -2667,4 +2670,4 @@ def generate(
             outputs.sequences = output_values
             return outputs
         else:
-            return output_values
+            return output_values

training/arguments.py

@@ -3,6 +3,7 @@
 
 from transformers import Seq2SeqTrainingArguments
 
+
 @dataclass
 class ModelArguments:
     """
@@ -67,15 +68,18 @@ class ModelArguments:
     )
     asr_model_name_or_path: str = field(
         default="distil-whisper/distil-large-v2",
-        metadata={"help": "Used to compute WER during evaluation. Path to pretrained model or model identifier from huggingface.co/models"}
+        metadata={
+            "help": "Used to compute WER during evaluation. Path to pretrained model or model identifier from huggingface.co/models"
+        },
     )
     clap_model_name_or_path: str = field(
         default="laion/larger_clap_music_and_speech",
-        metadata={"help": "Used to compute audio similarity during evaluation. Path to pretrained model or model identifier from huggingface.co/models"}
+        metadata={
+            "help": "Used to compute audio similarity during evaluation. Path to pretrained model or model identifier from huggingface.co/models"
+        },
     )
 
 
-
 @dataclass
 class DataTrainingArguments:
     """

training/data.py

@@ -11,6 +11,7 @@
 
 from accelerate import Accelerator
 
+
 @dataclass
 class DataCollatorEncodecWithPadding:
     """
@@ -301,4 +302,4 @@ def load_multiple_datasets(
         with accelerator.main_process_first():
             interleaved_dataset = concatenate_datasets(all_datasets)
 
-    return interleaved_dataset
+    return interleaved_dataset

training/eval.py

@@ -1,4 +1,4 @@
-import torch    
+import torch
 import evaluate
 from transformers import AutoModel, AutoProcessor, pipeline
 
@@ -20,6 +20,7 @@ def clap_similarity(clap_model_name_or_path, texts, audios, device):
     clap_inputs.to("cpu")
     return cosine_sim.mean().to("cpu")
 
+
 def wer(asr_model_name_or_path, prompts, audios, device, per_device_eval_batch_size, sampling_rate):
     metric = evaluate.load("wer")
     asr_pipeline = pipeline(model=asr_model_name_or_path, device=device)
@@ -32,4 +33,4 @@ def wer(asr_model_name_or_path, prompts, audios, device, per_device_eval_batch_s
         predictions=[t["text"].lower() for t in transcriptions], references=[t.lower() for t in prompts]
     )
 
-    return word_error, [t["text"] for t in transcriptions]
+    return word_error, [t["text"] for t in transcriptions]

training/run_parler_tts_training.py

@@ -21,7 +21,6 @@
 import re
 import sys
 import time
-from dataclasses import dataclass, field
 from datetime import timedelta
 
 from tqdm import tqdm
@@ -38,11 +37,7 @@
 from multiprocess import set_start_method
 from torch.utils.data import DataLoader
 from tqdm import tqdm
-from transformers import (
-    AutoFeatureExtractor,
-    AutoTokenizer,
-    HfArgumentParser
-)
+from transformers import AutoFeatureExtractor, AutoTokenizer, HfArgumentParser
 from transformers.trainer_pt_utils import LengthGroupedSampler
 from transformers.optimization import get_scheduler
 from transformers.trainer_pt_utils import LengthGroupedSampler
@@ -306,9 +301,7 @@ def main():
     # update pad token id and decoder_start_token_id
     config.update(
         {
-            "pad_token_id": model_args.pad_token_id
-            if model_args.pad_token_id is not None
-            else config.pad_token_id,
+            "pad_token_id": model_args.pad_token_id if model_args.pad_token_id is not None else config.pad_token_id,
             "decoder_start_token_id": model_args.decoder_start_token_id
             if model_args.decoder_start_token_id is not None
             else config.decoder_start_token_id,
@@ -579,16 +572,18 @@ def compute_metrics(audios, descriptions, prompts, device="cpu"):
         texts = description_tokenizer.batch_decode(input_ids, skip_special_tokens=True)
         prompts = prompt_tokenizer.batch_decode(prompts, skip_special_tokens=True)
         audios = [a.cpu().numpy() for a in audios]
-        
+
         clap_score = clap_similarity(model_args.clap_model_name_or_path, texts, audios, device)
         results["clap"] = clap_score
 
-        word_error, transcriptions = wer(model_args.asr_model_name_or_path,
-                                        prompts,
-                                        audios,
-                                        device,
-                                        training_args.per_device_eval_batch_size,
-                                        sampling_rate)
+        word_error, transcriptions = wer(
+            model_args.asr_model_name_or_path,
+            prompts,
+            audios,
+            device,
+            training_args.per_device_eval_batch_size,
+            sampling_rate,
+        )
         results["wer"] = word_error
 
         return results, texts, prompts, audios, transcriptions
@@ -878,7 +873,9 @@ def generate_step(batch):
                     accelerator.save_state(output_dir=intermediate_dir, safe_serialization=False)
                     accelerator.wait_for_everyone()
                     if accelerator.is_main_process:
-                        rotate_checkpoints(training_args.save_total_limit, output_dir=training_args.output_dir, logger=logger)
+                        rotate_checkpoints(
+                            training_args.save_total_limit, output_dir=training_args.output_dir, logger=logger
+                        )
 
                         if cur_step == total_train_steps:
                             # un-wrap student model for save
@@ -1020,4 +1017,4 @@ def generate_step(batch):
 
 if __name__ == "__main__":
     set_start_method("spawn")
-    main()
+    main()

training/utils.py

@@ -8,6 +8,7 @@
 import torch
 from wandb import Audio
 
+
 def list_field(default=None, metadata=None):
     return field(default_factory=lambda: default, metadata=metadata)
 
@@ -121,4 +122,4 @@ def log_pred(
                 ]
             },
             step=step,
-        )
+        )