mirror of https://github.com/coqui-ai/TTS.git
Add parameters to be able to set then on colab demo
This commit is contained in:
Edresson Casanova
parent
335b8c37b3
commit
eaa5355c91
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@ -8,9 +8,9 @@ from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrai
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from TTS.utils.manage import ModelManager
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def train_gpt(language, num_epochs, batch_size, train_csv, eval_csv, output_path):
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def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path):
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# Logging parameters
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RUN_NAME = "GPT_XTTSv2.1_FT"
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RUN_NAME = "GPT_XTTS_FT"
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PROJECT_NAME = "XTTS_trainer"
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DASHBOARD_LOGGER = "tensorboard"
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LOGGER_URI = None
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@ -18,13 +18,11 @@ def train_gpt(language, num_epochs, batch_size, train_csv, eval_csv, output_path
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# Set here the path that the checkpoints will be saved. Default: ./run/training/
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OUT_PATH = os.path.join(output_path, "run", "training")
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# Training Parameters
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OPTIMIZER_WD_ONLY_ON_WEIGHTS = True # for multi-gpu training please make it False
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START_WITH_EVAL = True # if True it will star with evaluation
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START_WITH_EVAL = False # if True it will star with evaluation
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BATCH_SIZE = batch_size # set here the batch size
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GRAD_ACUMM_STEPS = 1 # set here the grad accumulation steps
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# Note: we recommend that BATCH_SIZE * GRAD_ACUMM_STEPS need to be at least 252 for more efficient training. You can increase/decrease BATCH_SIZE but then set GRAD_ACUMM_STEPS accordingly.
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GRAD_ACUMM_STEPS = grad_acumm # set here the grad accumulation steps
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# Define here the dataset that you want to use for the fine-tuning on.
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@ -1,3 +1,4 @@
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import argparse
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import os
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import sys
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import tempfile
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@ -21,7 +22,6 @@ def clear_gpu_cache():
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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PORT = 5003
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XTTS_MODEL = None
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def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
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@ -101,231 +101,279 @@ def read_logs():
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return f.read()
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with gr.Blocks() as demo:
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with gr.Tab("Data processing"):
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out_path = gr.Textbox(
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label="Output path (where data and checkpoints will be saved):",
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value="/tmp/xtts_ft/"
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)
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# upload_file = gr.Audio(
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# sources="upload",
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# label="Select here the audio files that you want to use for XTTS trainining !",
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# type="filepath",
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# )
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upload_file = gr.File(
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file_count="multiple",
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label="Select here the audio files that you want to use for XTTS trainining (Supported formats: wav, mp3, and flac)",
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)
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lang = gr.Dropdown(
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label="Dataset Language",
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value="en",
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choices=[
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"en",
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"es",
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"fr",
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"de",
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"it",
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"pt",
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"pl",
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"tr",
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"ru",
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"nl",
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"cs",
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"ar",
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"zh",
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"hu",
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"ko",
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"ja"
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],
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)
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progress_data = gr.Label(
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label="Progress:"
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)
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logs = gr.Textbox(
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label="Logs:",
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interactive=False,
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)
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demo.load(read_logs, None, logs, every=1)
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prompt_compute_btn = gr.Button(value="Step 1 - Create dataset.")
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def preprocess_dataset(audio_path, language, out_path, progress=gr.Progress(track_tqdm=True)):
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clear_gpu_cache()
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out_path = os.path.join(out_path, "dataset")
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os.makedirs(out_path, exist_ok=True)
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if audio_path is None:
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# ToDo: raise an error
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pass
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else:
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train_meta, eval_meta, audio_total_size = format_audio_list(audio_path, target_language=language, out_path=out_path, gradio_progress=progress)
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clear_gpu_cache()
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# if audio total len is less than 2 minutes raise an error
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if audio_total_size < 120:
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message = "The sum of the duration of the audios that you provided should be at least 2 minutes!"
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print(message)
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return message, " ", " "
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print("Dataset Processed!")
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return "Dataset Processed!", train_meta, eval_meta
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with gr.Tab("Fine-tuning XTTS Encoder"):
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train_csv = gr.Textbox(
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label="Train CSV:",
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)
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eval_csv = gr.Textbox(
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label="Eval CSV:",
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)
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num_epochs = gr.Slider(
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label="num_epochs",
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minimum=1,
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maximum=100,
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step=1,
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value=10,
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)
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batch_size = gr.Slider(
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label="batch_size",
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minimum=2,
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maximum=512,
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step=1,
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value=4,
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)
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progress_train = gr.Label(
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label="Progress:"
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)
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logs_tts_train = gr.Textbox(
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label="Logs:",
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interactive=False,
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)
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demo.load(read_logs, None, logs_tts_train, every=1)
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train_btn = gr.Button(value="Step 2 - Run the training")
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def train_model(language, train_csv, eval_csv, num_epochs, batch_size, output_path, progress=gr.Progress(track_tqdm=True)):
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clear_gpu_cache()
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config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(language, num_epochs, batch_size, train_csv, eval_csv, output_path=output_path)
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# copy original files to avoid parameters changes issues
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os.system(f"cp {config_path} {exp_path}")
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os.system(f"cp {vocab_file} {exp_path}")
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ft_xtts_checkpoint = os.path.join(exp_path, "best_model.pth")
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print("Model training done!")
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clear_gpu_cache()
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return "Model training done!", config_path, vocab_file, ft_xtts_checkpoint, speaker_wav
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with gr.Tab("Inference"):
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with gr.Row():
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with gr.Column() as col1:
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xtts_checkpoint = gr.Textbox(
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label="XTTS checkpoint path:",
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value="",
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)
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xtts_config = gr.Textbox(
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label="XTTS config path:",
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value="",
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)
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xtts_vocab = gr.Textbox(
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label="XTTS config path:",
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value="",
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)
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progress_load = gr.Label(
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label="Progress:"
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)
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load_btn = gr.Button(value="Step 3 - Load Fine tuned XTTS model")
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with gr.Column() as col2:
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speaker_reference_audio = gr.Textbox(
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label="Speaker reference audio:",
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value="",
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)
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tts_language = gr.Dropdown(
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label="Language",
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value="en",
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choices=[
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"en",
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"es",
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"fr",
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"de",
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"it",
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"pt",
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"pl",
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"tr",
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"ru",
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"nl",
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"cs",
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"ar",
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"zh",
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"hu",
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"ko",
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"ja",
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]
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)
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tts_text = gr.Textbox(
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label="Input Text.",
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value="This model sounds really good and above all, it's reasonably fast.",
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)
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tts_btn = gr.Button(value="Step 4 - Inference")
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with gr.Column() as col3:
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tts_output_audio = gr.Audio(label="Generated Audio.")
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reference_audio = gr.Audio(label="Reference audio used.")
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prompt_compute_btn.click(
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fn=preprocess_dataset,
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inputs=[
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upload_file,
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lang,
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out_path,
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],
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outputs=[
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progress_data,
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train_csv,
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eval_csv,
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],
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)
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train_btn.click(
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fn=train_model,
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inputs=[
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lang,
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train_csv,
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eval_csv,
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num_epochs,
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batch_size,
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out_path,
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],
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outputs=[progress_train, xtts_config, xtts_vocab, xtts_checkpoint, speaker_reference_audio],
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)
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load_btn.click(
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fn=load_model,
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inputs=[
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xtts_checkpoint,
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xtts_config,
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xtts_vocab
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],
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outputs=[progress_load],
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)
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tts_btn.click(
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fn=run_tts,
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inputs=[
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tts_language,
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tts_text,
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speaker_reference_audio,
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],
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outputs=[tts_output_audio, reference_audio],
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)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="""XTTS fine-tuning demo\n\n"""
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"""
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Example runs:
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python3 TTS/demos/xtts_ft_demo/xtts_demo.py --port
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""",
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formatter_class=argparse.RawTextHelpFormatter,
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)
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parser.add_argument(
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"--port",
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type=int,
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help="Port to run the gradio demo. Default: 5003",
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default=5003,
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)
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parser.add_argument(
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"--out_path",
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type=str,
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help="Output path (where data and checkpoints will be saved) Default: /tmp/xtts_ft/",
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default="/tmp/xtts_ft/",
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)
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parser.add_argument(
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"--num_epochs",
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type=int,
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help="Number of epochs to train. Default: 10",
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default=10,
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)
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parser.add_argument(
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"--batch_size",
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type=int,
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help="Batch size. Default: 4",
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default=4,
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)
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parser.add_argument(
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"--grad_acumm",
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type=int,
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help="Grad accumulation steps. Default: 1",
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default=1,
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)
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args = parser.parse_args()
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with gr.Blocks() as demo:
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with gr.Tab("Data processing"):
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out_path = gr.Textbox(
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label="Output path (where data and checkpoints will be saved):",
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value=args.out_path,
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)
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# upload_file = gr.Audio(
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# sources="upload",
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# label="Select here the audio files that you want to use for XTTS trainining !",
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# type="filepath",
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# )
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upload_file = gr.File(
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file_count="multiple",
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label="Select here the audio files that you want to use for XTTS trainining (Supported formats: wav, mp3, and flac)",
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)
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lang = gr.Dropdown(
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label="Dataset Language",
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value="en",
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choices=[
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"en",
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"es",
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"fr",
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"de",
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"it",
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"pt",
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"pl",
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"tr",
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"ru",
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"nl",
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"cs",
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"ar",
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"zh",
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"hu",
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"ko",
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"ja"
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],
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)
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progress_data = gr.Label(
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label="Progress:"
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)
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logs = gr.Textbox(
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label="Logs:",
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interactive=False,
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)
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demo.load(read_logs, None, logs, every=1)
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prompt_compute_btn = gr.Button(value="Step 1 - Create dataset.")
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def preprocess_dataset(audio_path, language, out_path, progress=gr.Progress(track_tqdm=True)):
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clear_gpu_cache()
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out_path = os.path.join(out_path, "dataset")
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os.makedirs(out_path, exist_ok=True)
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if audio_path is None:
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# ToDo: raise an error
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pass
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else:
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train_meta, eval_meta, audio_total_size = format_audio_list(audio_path, target_language=language, out_path=out_path, gradio_progress=progress)
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clear_gpu_cache()
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# if audio total len is less than 2 minutes raise an error
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if audio_total_size < 120:
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message = "The sum of the duration of the audios that you provided should be at least 2 minutes!"
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print(message)
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return message, " ", " "
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print("Dataset Processed!")
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return "Dataset Processed!", train_meta, eval_meta
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with gr.Tab("Fine-tuning XTTS Encoder"):
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train_csv = gr.Textbox(
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label="Train CSV:",
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)
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eval_csv = gr.Textbox(
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label="Eval CSV:",
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)
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num_epochs = gr.Slider(
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label="Number of epochs:",
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minimum=1,
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maximum=100,
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step=1,
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value=args.num_epochs,
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)
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batch_size = gr.Slider(
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label="Batch size:",
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minimum=2,
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maximum=512,
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step=1,
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value=args.batch_size,
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)
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grad_acumm = gr.Slider(
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label="Grad accumulation steps:",
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minimum=2,
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maximum=128,
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step=1,
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value=args.grad_acumm,
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)
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progress_train = gr.Label(
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label="Progress:"
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)
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logs_tts_train = gr.Textbox(
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label="Logs:",
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interactive=False,
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)
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demo.load(read_logs, None, logs_tts_train, every=1)
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train_btn = gr.Button(value="Step 2 - Run the training")
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def train_model(language, train_csv, eval_csv, num_epochs, batch_size, grad_acumm, output_path):
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clear_gpu_cache()
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config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path=output_path)
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# copy original files to avoid parameters changes issues
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os.system(f"cp {config_path} {exp_path}")
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os.system(f"cp {vocab_file} {exp_path}")
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ft_xtts_checkpoint = os.path.join(exp_path, "best_model.pth")
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print("Model training done!")
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clear_gpu_cache()
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return "Model training done!", config_path, vocab_file, ft_xtts_checkpoint, speaker_wav
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with gr.Tab("Inference"):
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with gr.Row():
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with gr.Column() as col1:
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xtts_checkpoint = gr.Textbox(
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label="XTTS checkpoint path:",
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value="",
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)
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xtts_config = gr.Textbox(
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label="XTTS config path:",
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value="",
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)
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xtts_vocab = gr.Textbox(
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label="XTTS config path:",
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value="",
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)
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progress_load = gr.Label(
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label="Progress:"
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)
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load_btn = gr.Button(value="Step 3 - Load Fine tuned XTTS model")
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with gr.Column() as col2:
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speaker_reference_audio = gr.Textbox(
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label="Speaker reference audio:",
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value="",
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)
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tts_language = gr.Dropdown(
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label="Language",
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value="en",
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choices=[
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"en",
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"es",
|
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"fr",
|
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"de",
|
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"it",
|
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"pt",
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"pl",
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"tr",
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"ru",
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"nl",
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"cs",
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"ar",
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"zh",
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"hu",
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"ko",
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"ja",
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]
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)
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tts_text = gr.Textbox(
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label="Input Text.",
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value="This model sounds really good and above all, it's reasonably fast.",
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)
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tts_btn = gr.Button(value="Step 4 - Inference")
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with gr.Column() as col3:
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tts_output_audio = gr.Audio(label="Generated Audio.")
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reference_audio = gr.Audio(label="Reference audio used.")
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prompt_compute_btn.click(
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fn=preprocess_dataset,
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inputs=[
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upload_file,
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lang,
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out_path,
|
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],
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outputs=[
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progress_data,
|
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train_csv,
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eval_csv,
|
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],
|
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)
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|
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train_btn.click(
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fn=train_model,
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inputs=[
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lang,
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train_csv,
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eval_csv,
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num_epochs,
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batch_size,
|
||||
grad_acumm,
|
||||
out_path,
|
||||
],
|
||||
outputs=[progress_train, xtts_config, xtts_vocab, xtts_checkpoint, speaker_reference_audio],
|
||||
)
|
||||
|
||||
load_btn.click(
|
||||
fn=load_model,
|
||||
inputs=[
|
||||
xtts_checkpoint,
|
||||
xtts_config,
|
||||
xtts_vocab
|
||||
],
|
||||
outputs=[progress_load],
|
||||
)
|
||||
|
||||
tts_btn.click(
|
||||
fn=run_tts,
|
||||
inputs=[
|
||||
tts_language,
|
||||
tts_text,
|
||||
speaker_reference_audio,
|
||||
],
|
||||
outputs=[tts_output_audio, reference_audio],
|
||||
)
|
||||
|
||||
demo.launch(
|
||||
share=True,
|
||||
debug=True,
|
||||
server_port=PORT,
|
||||
debug=False,
|
||||
server_port=args.port,
|
||||
server_name="0.0.0.0"
|
||||
)
|
||||
|
|
|
|||
Loading…
Reference in New Issue