Medix-AI/coqui-tts
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coqui-tts
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Uses tabs instead of columns

This commit is contained in:
Edresson Casanova 2023-11-23 17:50:41 -03:00
parent cc4f37e1b0
commit 7cc348ed76
2 changed files with 115 additions and 115 deletions
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1
TTS/demos/xtts_ft_demo/requirements.txt Normal file
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@ -0,0 +1 @@
faster_whisper

229
TTS/demos/xtts_ft_demo/xtts_demo.py
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@ -95,123 +95,46 @@ def read_logs():
with gr.Blocks() as demo: with gr.Blocks() as demo:
with gr.Tab("XTTS"): state_vars = gr.State()
state_vars = gr.State( with gr.Tab("Data processing"):
upload_file = gr.Audio(
sources="upload",
label="Select here the audio files that you want to use for XTTS trainining !",
type="filepath",
) )
with gr.Row(): lang = gr.Dropdown(
with gr.Column() as col1: label="Dataset Language",
upload_file = gr.Audio( value="en",
sources="upload", choices=[
label="Select here the audio files that you want to use for XTTS trainining !", "en",
type="filepath", "es",
) "fr",
lang = gr.Dropdown( "de",
label="Dataset Language", "it",
value="en", "pt",
choices=[ "pl",
"en", "tr",
"es", "ru",
"fr", "nl",
"de", "cs",
"it", "ar",
"pt", "zh",
"pl", "hu",
"tr", "ko",
"ru", "ja"
"nl", ],
"cs", )
"ar", progress_data = gr.Label(
"zh", label="Progress:"
"hu", )
"ko", logs = gr.Textbox(
"ja" label="Logs:",
], interactive=False,
) )
progress_data = gr.Label( demo.load(read_logs, None, logs, every=1)
label="Progress:"
)
logs = gr.Textbox(
label="Logs:",
interactive=False,
)
demo.load(read_logs, None, logs, every=1)
prompt_compute_btn = gr.Button(value="Step 1 - Create dataset.")
with gr.Column() as col2:
num_epochs = gr.Slider(
label="num_epochs",
minimum=1,
maximum=100,
step=1,
value=2,# 15
)
batch_size = gr.Slider(
label="batch_size",
minimum=2,
maximum=512,
step=1,
value=15,
)
progress_train = gr.Label(
label="Progress:"
)
logs_tts_train = gr.Textbox(
label="Logs:",
interactive=False,
)
demo.load(read_logs, None, logs_tts_train, every=1)
train_btn = gr.Button(value="Step 2 - Run the training")
with gr.Column() as col3:
xtts_checkpoint = gr.Textbox(
label="XTTS checkpoint path:",
value="",
)
xtts_config = gr.Textbox(
label="XTTS config path:",
value="",
)
xtts_vocab = gr.Textbox(
label="XTTS config path:",
value="",
)
speaker_reference_audio = gr.Textbox(
label="Speaker reference audio:",
value="",
)
tts_language = gr.Dropdown(
label="Language",
value="en",
choices=[
"en",
"es",
"fr",
"de",
"it",
"pt",
"pl",
"tr",
"ru",
"nl",
"cs",
"ar",
"zh",
"hu",
"ko",
"ja",
]
)
tts_text = gr.Textbox(
label="Input Text.",
value="This model sounds really good and above all, it's reasonably fast.",
)
tts_btn = gr.Button(value="Step 3 - Inference XTTS model")
tts_output_audio = gr.Audio(label="Generated Audio.")
reference_audio = gr.Audio(label="Reference audio used.")
prompt_compute_btn = gr.Button(value="Step 1 - Create dataset.")
def preprocess_dataset(audio_path, language, state_vars, progress=gr.Progress(track_tqdm=True)): def preprocess_dataset(audio_path, language, state_vars, progress=gr.Progress(track_tqdm=True)):
# create a temp directory to save the dataset # create a temp directory to save the dataset
out_path = tempfile.TemporaryDirectory().name out_path = tempfile.TemporaryDirectory().name
@ -240,6 +163,32 @@ with gr.Blocks() as demo:
], ],
) )
with gr.Tab("Fine-tuning XTTS"):
num_epochs = gr.Slider(
label="num_epochs",
minimum=1,
maximum=100,
step=1,
value=2,# 15
)
batch_size = gr.Slider(
label="batch_size",
minimum=2,
maximum=512,
step=1,
value=15,
)
progress_train = gr.Label(
label="Progress:"
)
logs_tts_train = gr.Textbox(
label="Logs:",
interactive=False,
)
demo.load(read_logs, None, logs_tts_train, every=1)
train_btn = gr.Button(value="Step 2 - Run the training")
def train_model(language, num_epochs, batch_size, state_vars, output_path="./", progress=gr.Progress(track_tqdm=True)): def train_model(language, num_epochs, batch_size, state_vars, output_path="./", progress=gr.Progress(track_tqdm=True)):
# state_vars = {'train_csv': '/tmp/tmprh4k_vou/metadata_train.csv', 'eval_csv': '/tmp/tmprh4k_vou/metadata_eval.csv'} # state_vars = {'train_csv': '/tmp/tmprh4k_vou/metadata_train.csv', 'eval_csv': '/tmp/tmprh4k_vou/metadata_eval.csv'}
@ -257,6 +206,55 @@ with gr.Blocks() as demo:
return "Model training done!", state_vars, config_path, vocab_file, ft_xtts_checkpoint, speaker_wav return "Model training done!", state_vars, config_path, vocab_file, ft_xtts_checkpoint, speaker_wav
with gr.Tab("Inference"):
xtts_checkpoint = gr.Textbox(
label="XTTS checkpoint path:",
value="",
)
xtts_config = gr.Textbox(
label="XTTS config path:",
value="",
)
xtts_vocab = gr.Textbox(
label="XTTS config path:",
value="",
)
speaker_reference_audio = gr.Textbox(
label="Speaker reference audio:",
value="",
)
tts_language = gr.Dropdown(
label="Language",
value="en",
choices=[
"en",
"es",
"fr",
"de",
"it",
"pt",
"pl",
"tr",
"ru",
"nl",
"cs",
"ar",
"zh",
"hu",
"ko",
"ja",
]
)
tts_text = gr.Textbox(
label="Input Text.",
value="This model sounds really good and above all, it's reasonably fast.",
)
tts_btn = gr.Button(value="Step 3 - Inference XTTS model")
tts_output_audio = gr.Audio(label="Generated Audio.")
reference_audio = gr.Audio(label="Reference audio used.")
train_btn.click( train_btn.click(
fn=train_model, fn=train_model,
inputs=[ inputs=[
@ -268,6 +266,7 @@ with gr.Blocks() as demo:
outputs=[progress_train, state_vars, xtts_config, xtts_vocab, xtts_checkpoint, speaker_reference_audio], outputs=[progress_train, state_vars, xtts_config, xtts_vocab, xtts_checkpoint, speaker_reference_audio],
) )
tts_btn.click( tts_btn.click(
fn=run_tts, fn=run_tts,
inputs=[ inputs=[