Run make style

TTS/demos/xtts_ft_demo/utils/formatter.py

@@ -19,9 +19,10 @@ def list_audios(basePath, contains=None):
     # return the set of files that are valid
     return list_files(basePath, validExts=audio_types, contains=contains)
 
+
 def list_files(basePath, validExts=None, contains=None):
     # loop over the directory structure
-    for (rootDir, dirNames, filenames) in os.walk(basePath):
+    for rootDir, dirNames, filenames in os.walk(basePath):
         # loop over the filenames in the current directory
         for filename in filenames:
             # if the contains string is not none and the filename does not contain
@@ -30,15 +31,24 @@ def list_files(basePath, validExts=None, contains=None):
                 continue
 
             # determine the file extension of the current file
-            ext = filename[filename.rfind("."):].lower()
+            ext = filename[filename.rfind(".") :].lower()
 
             # check to see if the file is an audio and should be processed
             if validExts is None or ext.endswith(validExts):
                 # construct the path to the audio and yield it
                 audioPath = os.path.join(rootDir, filename)
                 yield audioPath
 
-def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0.2, eval_percentage=0.15, speaker_name="coqui", gradio_progress=None):
+
+def format_audio_list(
+    audio_files,
+    target_language="en",
+    out_path=None,
+    buffer=0.2,
+    eval_percentage=0.15,
+    speaker_name="coqui",
+    gradio_progress=None,
+):
     audio_total_size = 0
     # make sure that ooutput file exists
     os.makedirs(out_path, exist_ok=True)
@@ -63,7 +73,7 @@ def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0
             wav = torch.mean(wav, dim=0, keepdim=True)
 
         wav = wav.squeeze()
-        audio_total_size += (wav.size(-1) / sr)
+        audio_total_size += wav.size(-1) / sr
 
         segments, _ = asr_model.transcribe(audio_path, word_timestamps=True, language=target_language)
         segments = list(segments)
@@ -88,7 +98,7 @@ def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0
                     # get previous sentence end
                     previous_word_end = words_list[word_idx - 1].end
                     # add buffer or get the silence midle between the previous sentence and the current one
-                    sentence_start = max(sentence_start - buffer, (previous_word_end + sentence_start)/2)
+                    sentence_start = max(sentence_start - buffer, (previous_word_end + sentence_start) / 2)
 
                 sentence = word.word
                 first_word = False
@@ -112,19 +122,16 @@ def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0
 
                 # Average the current word end and next word start
                 word_end = min((word.end + next_word_start) / 2, word.end + buffer)
-                
+
                 absoulte_path = os.path.join(out_path, audio_file)
                 os.makedirs(os.path.dirname(absoulte_path), exist_ok=True)
                 i += 1
                 first_word = True
 
-                audio = wav[int(sr*sentence_start):int(sr*word_end)].unsqueeze(0)
+                audio = wav[int(sr * sentence_start) : int(sr * word_end)].unsqueeze(0)
                 # if the audio is too short ignore it (i.e < 0.33 seconds)
-                if audio.size(-1) >= sr/3:
-                    torchaudio.save(absoulte_path,
-                        audio,
-                        sr
-                    )
+                if audio.size(-1) >= sr / 3:
+                    torchaudio.save(absoulte_path, audio, sr)
                 else:
                     continue
 
@@ -134,21 +141,21 @@ def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0
 
     df = pandas.DataFrame(metadata)
     df = df.sample(frac=1)
-    num_val_samples = int(len(df)*eval_percentage)
+    num_val_samples = int(len(df) * eval_percentage)
 
     df_eval = df[:num_val_samples]
     df_train = df[num_val_samples:]
 
-    df_train = df_train.sort_values('audio_file')
+    df_train = df_train.sort_values("audio_file")
     train_metadata_path = os.path.join(out_path, "metadata_train.csv")
     df_train.to_csv(train_metadata_path, sep="|", index=False)
 
     eval_metadata_path = os.path.join(out_path, "metadata_eval.csv")
-    df_eval = df_eval.sort_values('audio_file')
+    df_eval = df_eval.sort_values("audio_file")
     df_eval.to_csv(eval_metadata_path, sep="|", index=False)
 
     # deallocate VRAM and RAM
     del asr_model, df_train, df_eval, df, metadata
     gc.collect()
 
-    return train_metadata_path, eval_metadata_path, audio_total_size
+    return train_metadata_path, eval_metadata_path, audio_total_size

TTS/demos/xtts_ft_demo/utils/gpt_train.py

@@ -25,7 +25,6 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
     BATCH_SIZE = batch_size  # set here the batch size
     GRAD_ACUMM_STEPS = grad_acumm  # set here the grad accumulation steps
 
-
     # Define here the dataset that you want to use for the fine-tuning on.
     config_dataset = BaseDatasetConfig(
         formatter="coqui",
@@ -43,7 +42,6 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
     CHECKPOINTS_OUT_PATH = os.path.join(OUT_PATH, "XTTS_v2.0_original_model_files/")
     os.makedirs(CHECKPOINTS_OUT_PATH, exist_ok=True)
 
-
     # DVAE files
     DVAE_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/dvae.pth"
     MEL_NORM_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/mel_stats.pth"
@@ -55,8 +53,9 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
     # download DVAE files if needed
     if not os.path.isfile(DVAE_CHECKPOINT) or not os.path.isfile(MEL_NORM_FILE):
         print(" > Downloading DVAE files!")
-        ModelManager._download_model_files([MEL_NORM_LINK, DVAE_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True)
-
+        ModelManager._download_model_files(
+            [MEL_NORM_LINK, DVAE_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True
+        )
 
     # Download XTTS v2.0 checkpoint if needed
     TOKENIZER_FILE_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/vocab.json"
@@ -160,7 +159,7 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
 
     # get the longest text audio file to use as speaker reference
     samples_len = [len(item["text"].split(" ")) for item in train_samples]
-    longest_text_idx =  samples_len.index(max(samples_len))
+    longest_text_idx = samples_len.index(max(samples_len))
     speaker_ref = train_samples[longest_text_idx]["audio_file"]
 
     trainer_out_path = trainer.output_path