Merge branch 'coqui-ai:dev' into dev

TTS/VERSION

@@ -1 +1 @@
-0.21.2
+0.21.3

TTS/demos/xtts_ft_demo/requirements.txt

@@ -0,0 +1,2 @@
+faster_whisper==0.9.0
+gradio==4.7.1

TTS/demos/xtts_ft_demo/utils/formatter.py

@@ -0,0 +1,160 @@
+import os
+import gc
+import torchaudio
+import pandas
+from faster_whisper import WhisperModel
+from glob import glob
+
+from tqdm import tqdm
+
+import torch
+import torchaudio
+# torch.set_num_threads(1)
+
+from TTS.tts.layers.xtts.tokenizer import multilingual_cleaners
+
+torch.set_num_threads(16)
+
+
+import os
+
+audio_types = (".wav", ".mp3", ".flac")
+
+
+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):
+        # 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
+            # the supplied string, then ignore the file
+            if contains is not None and filename.find(contains) == -1:
+                continue
+
+            # determine the file extension of the current file
+            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):
+    audio_total_size = 0
+    # make sure that ooutput file exists
+    os.makedirs(out_path, exist_ok=True)
+
+    # Loading Whisper
+    device = "cuda" if torch.cuda.is_available() else "cpu" 
+
+    print("Loading Whisper Model!")
+    asr_model = WhisperModel("large-v2", device=device, compute_type="float16")
+
+    metadata = {"audio_file": [], "text": [], "speaker_name": []}
+
+    if gradio_progress is not None:
+        tqdm_object = gradio_progress.tqdm(audio_files, desc="Formatting...")
+    else:
+        tqdm_object = tqdm(audio_files)
+
+    for audio_path in tqdm_object:
+        wav, sr = torchaudio.load(audio_path)
+        # stereo to mono if needed
+        if wav.size(0) != 1:
+            wav = torch.mean(wav, dim=0, keepdim=True)
+
+        wav = wav.squeeze()
+        audio_total_size += (wav.size(-1) / sr)
+
+        segments, _ = asr_model.transcribe(audio_path, word_timestamps=True, language=target_language)
+        segments = list(segments)
+        i = 0
+        sentence = ""
+        sentence_start = None
+        first_word = True
+        # added all segments words in a unique list
+        words_list = []
+        for _, segment in enumerate(segments):
+            words = list(segment.words)
+            words_list.extend(words)
+
+        # process each word
+        for word_idx, word in enumerate(words_list):
+            if first_word:
+                sentence_start = word.start
+                # If it is the first sentence, add buffer or get the begining of the file
+                if word_idx == 0:
+                    sentence_start = max(sentence_start - buffer, 0)  # Add buffer to the sentence start
+                else:
+                    # 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 = word.word
+                first_word = False
+            else:
+                sentence += word.word
+
+            if word.word[-1] in ["!", ".", "?"]:
+                sentence = sentence[1:]
+                # Expand number and abbreviations plus normalization
+                sentence = multilingual_cleaners(sentence, target_language)
+                audio_file_name, _ = os.path.splitext(os.path.basename(audio_path))
+
+                audio_file = f"wavs/{audio_file_name}_{str(i).zfill(8)}.wav"
+
+                # Check for the next word's existence
+                if word_idx + 1 < len(words_list):
+                    next_word_start = words_list[word_idx + 1].start
+                else:
+                    # If don't have more words it means that it is the last sentence then use the audio len as next word start
+                    next_word_start = (wav.shape[0] - 1) / sr
+
+                # 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)
+                # 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
+                    )
+                else:
+                    continue
+
+                metadata["audio_file"].append(audio_file)
+                metadata["text"].append(sentence)
+                metadata["speaker_name"].append(speaker_name)
+
+    df = pandas.DataFrame(metadata)
+    df = df.sample(frac=1)
+    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')
+    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.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

TTS/demos/xtts_ft_demo/utils/gpt_train.py

@@ -0,0 +1,172 @@
+import os
+import gc
+
+from trainer import Trainer, TrainerArgs
+
+from TTS.config.shared_configs import BaseDatasetConfig
+from TTS.tts.datasets import load_tts_samples
+from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
+from TTS.utils.manage import ModelManager
+
+
+def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path, max_audio_length=255995):
+    #  Logging parameters
+    RUN_NAME = "GPT_XTTS_FT"
+    PROJECT_NAME = "XTTS_trainer"
+    DASHBOARD_LOGGER = "tensorboard"
+    LOGGER_URI = None
+
+    # Set here the path that the checkpoints will be saved. Default: ./run/training/
+    OUT_PATH = os.path.join(output_path, "run", "training")
+
+    # Training Parameters
+    OPTIMIZER_WD_ONLY_ON_WEIGHTS = True  # for multi-gpu training please make it False
+    START_WITH_EVAL = False  # if True it will star with evaluation
+    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",
+        dataset_name="ft_dataset",
+        path=os.path.dirname(train_csv),
+        meta_file_train=train_csv,
+        meta_file_val=eval_csv,
+        language=language,
+    )
+
+    # Add here the configs of the datasets
+    DATASETS_CONFIG_LIST = [config_dataset]
+
+    # Define the path where XTTS v2.0.1 files will be downloaded
+    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"
+
+    # Set the path to the downloaded files
+    DVAE_CHECKPOINT = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(DVAE_CHECKPOINT_LINK))
+    MEL_NORM_FILE = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(MEL_NORM_LINK))
+
+    # 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)
+
+
+    # Download XTTS v2.0 checkpoint if needed
+    TOKENIZER_FILE_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/vocab.json"
+    XTTS_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/model.pth"
+    XTTS_CONFIG_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/config.json"
+
+    # XTTS transfer learning parameters: You we need to provide the paths of XTTS model checkpoint that you want to do the fine tuning.
+    TOKENIZER_FILE = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(TOKENIZER_FILE_LINK))  # vocab.json file
+    XTTS_CHECKPOINT = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(XTTS_CHECKPOINT_LINK))  # model.pth file
+    XTTS_CONFIG_FILE = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(XTTS_CONFIG_LINK))  # config.json file
+
+    # download XTTS v2.0 files if needed
+    if not os.path.isfile(TOKENIZER_FILE) or not os.path.isfile(XTTS_CHECKPOINT):
+        print(" > Downloading XTTS v2.0 files!")
+        ModelManager._download_model_files(
+            [TOKENIZER_FILE_LINK, XTTS_CHECKPOINT_LINK, XTTS_CONFIG_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True
+        )
+
+    # init args and config
+    model_args = GPTArgs(
+        max_conditioning_length=132300,  # 6 secs
+        min_conditioning_length=66150,  # 3 secs
+        debug_loading_failures=False,
+        max_wav_length=max_audio_length,  # ~11.6 seconds
+        max_text_length=200,
+        mel_norm_file=MEL_NORM_FILE,
+        dvae_checkpoint=DVAE_CHECKPOINT,
+        xtts_checkpoint=XTTS_CHECKPOINT,  # checkpoint path of the model that you want to fine-tune
+        tokenizer_file=TOKENIZER_FILE,
+        gpt_num_audio_tokens=1026,
+        gpt_start_audio_token=1024,
+        gpt_stop_audio_token=1025,
+        gpt_use_masking_gt_prompt_approach=True,
+        gpt_use_perceiver_resampler=True,
+    )
+    # define audio config
+    audio_config = XttsAudioConfig(sample_rate=22050, dvae_sample_rate=22050, output_sample_rate=24000)
+    # training parameters config
+    config = GPTTrainerConfig(
+        epochs=num_epochs,
+        output_path=OUT_PATH,
+        model_args=model_args,
+        run_name=RUN_NAME,
+        project_name=PROJECT_NAME,
+        run_description="""
+            GPT XTTS training
+            """,
+        dashboard_logger=DASHBOARD_LOGGER,
+        logger_uri=LOGGER_URI,
+        audio=audio_config,
+        batch_size=BATCH_SIZE,
+        batch_group_size=48,
+        eval_batch_size=BATCH_SIZE,
+        num_loader_workers=8,
+        eval_split_max_size=256,
+        print_step=50,
+        plot_step=100,
+        log_model_step=100,
+        save_step=1000,
+        save_n_checkpoints=1,
+        save_checkpoints=True,
+        # target_loss="loss",
+        print_eval=False,
+        # Optimizer values like tortoise, pytorch implementation with modifications to not apply WD to non-weight parameters.
+        optimizer="AdamW",
+        optimizer_wd_only_on_weights=OPTIMIZER_WD_ONLY_ON_WEIGHTS,
+        optimizer_params={"betas": [0.9, 0.96], "eps": 1e-8, "weight_decay": 1e-2},
+        lr=5e-06,  # learning rate
+        lr_scheduler="MultiStepLR",
+        # it was adjusted accordly for the new step scheme
+        lr_scheduler_params={"milestones": [50000 * 18, 150000 * 18, 300000 * 18], "gamma": 0.5, "last_epoch": -1},
+        test_sentences=[],
+    )
+
+    # init the model from config
+    model = GPTTrainer.init_from_config(config)
+
+    # load training samples
+    train_samples, eval_samples = load_tts_samples(
+        DATASETS_CONFIG_LIST,
+        eval_split=True,
+        eval_split_max_size=config.eval_split_max_size,
+        eval_split_size=config.eval_split_size,
+    )
+
+    # init the trainer and 🚀
+    trainer = Trainer(
+        TrainerArgs(
+            restore_path=None,  # xtts checkpoint is restored via xtts_checkpoint key so no need of restore it using Trainer restore_path parameter
+            skip_train_epoch=False,
+            start_with_eval=START_WITH_EVAL,
+            grad_accum_steps=GRAD_ACUMM_STEPS,
+        ),
+        config,
+        output_path=OUT_PATH,
+        model=model,
+        train_samples=train_samples,
+        eval_samples=eval_samples,
+    )
+    trainer.fit()
+
+    # 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))
+    speaker_ref = train_samples[longest_text_idx]["audio_file"]
+
+    trainer_out_path = trainer.output_path
+
+    # deallocate VRAM and RAM
+    del model, trainer, train_samples, eval_samples
+    gc.collect()
+
+    return XTTS_CONFIG_FILE, XTTS_CHECKPOINT, TOKENIZER_FILE, trainer_out_path, speaker_ref