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gradio: added giga330M_TTSEnhanced model, changed default top_p to 0.9

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Stepan Zuev 2024-04-07 00:21:28 +03:00
parent 6afe1d8892
commit 9ce26becea
1 changed files with 9 additions and 3 deletions
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12
gradio_app.py
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@ -77,6 +77,9 @@ class WhisperxModel:
def load_models(whisper_backend_name, whisper_model_name, alignment_model_name, voicecraft_model_name):
global transcribe_model, align_model, voicecraft_model
if voicecraft_model_name == "giga330M_TTSEnhanced":
voicecraft_model_name = "gigaHalfLibri330M_TTSEnhanced_max16s"
if alignment_model_name is not None:
align_model = WhisperxAlignModel()
@ -433,7 +436,8 @@ with gr.Blocks() as app:
with gr.Column(scale=5):
with gr.Accordion("Select models", open=False) as models_selector:
with gr.Row():
voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M", choices=["giga330M", "giga830M"])
voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M",
choices=["giga330M", "giga830M", "giga330M_TTSEnhanced"])
whisper_backend_choice = gr.Radio(label="Whisper backend", value="whisperX", choices=["whisper", "whisperX"])
whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
choices=[None, "base.en", "small.en", "medium.en", "large"])
@ -498,13 +502,15 @@ with gr.Blocks() as app:
stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3, 4], value=3,
info="if there are long silence in the generated audio, reduce the stop_repetition to 2 or 1. -1 = disabled")
sample_batch_size = gr.Number(label="speech rate", value=4, precision=0,
info="The higher the number, the faster the output will be. Under the hood, the model will generate this many samples and choose the shortest one")
info="The higher the number, the faster the output will be. "
"Under the hood, the model will generate this many samples and choose the shortest one. "
"For giga330M_TTSEnhanced, 1 or 2 should be fine since the model is trained to do TTS.")
seed = gr.Number(label="seed", value=-1, precision=0, info="random seeds always works :)")
kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
info="set to 0 to use less VRAM, but with slower inference")
left_margin = gr.Number(label="left_margin", value=0.08, info="margin to the left of the editing segment")
right_margin = gr.Number(label="right_margin", value=0.08, info="margin to the right of the editing segment")
top_p = gr.Number(label="top_p", value=0.8, info="0.8 is a good value, 0.9 is also good")
top_p = gr.Number(label="top_p", value=0.9, info="0.9 is a good value, 0.8 is also good")
temperature = gr.Number(label="temperature", value=1, info="haven't try other values, do not recommend to change")
top_k = gr.Number(label="top_k", value=0, info="0 means we don't use topk sampling, because we use topp sampling")
codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000, info='encodec specific, Do not change')