mirror of
https://github.com/jasonppy/VoiceCraft.git
synced 2025-06-05 21:49:11 +02:00
essential gradio app args added, colab notebook fix
This commit is contained in:
Stepan Zuev
313
gradio_app.py
313
gradio_app.py
@@ -12,12 +12,10 @@ import numpy as np
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import random
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import uuid
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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os.chdir("/content/VoiceCraft-gradio-colab")
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os.environ['USER'] = 'aaa'
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DEMO_PATH = os.getenv("DEMO_PATH", ".demo")
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TMP_PATH = os.getenv("TMP_PATH", "./demo/temp")
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MODELS_PATH = os.getenv("MODELS_PATH", "./pretrained_models")
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device = "cuda" if torch.cuda.is_available() else "cpu"
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whisper_model, align_model, voicecraft_model = None, None, None
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@@ -94,14 +92,14 @@ def load_models(whisper_backend_name, whisper_model_name, alignment_model_name,
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transcribe_model = WhisperxModel(whisper_model_name, align_model)
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voicecraft_name = f"{voicecraft_model_name}.pth"
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ckpt_fn = f"./pretrained_models/{voicecraft_name}"
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encodec_fn = "./pretrained_models/encodec_4cb2048_giga.th"
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ckpt_fn = f"{MODELS_PATH}/{voicecraft_name}"
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encodec_fn = f"{MODELS_PATH}/encodec_4cb2048_giga.th"
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if not os.path.exists(ckpt_fn):
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os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/{voicecraft_name}\?download\=true")
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os.system(f"mv {voicecraft_name}\?download\=true ./pretrained_models/{voicecraft_name}")
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os.system(f"mv {voicecraft_name}\?download\=true {MODELS_PATH}/{voicecraft_name}")
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if not os.path.exists(encodec_fn):
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os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th")
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os.system(f"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th")
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os.system(f"mv encodec_4cb2048_giga.th {MODELS_PATH}/encodec_4cb2048_giga.th")
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ckpt = torch.load(ckpt_fn, map_location="cpu")
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model = voicecraft.VoiceCraft(ckpt["config"])
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@@ -431,146 +429,131 @@ def update_demo(mode, smart_transcript, edit_word_mode, transcript, edit_from_wo
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]
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with gr.Blocks() as app:
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with gr.Row():
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with gr.Column(scale=2):
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load_models_btn = gr.Button(value="Load models")
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with gr.Column(scale=5):
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with gr.Accordion("Select models", open=False) as models_selector:
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with gr.Row():
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voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M",
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choices=["giga330M", "giga830M", "giga330M_TTSEnhanced"])
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whisper_backend_choice = gr.Radio(label="Whisper backend", value="whisperX", choices=["whisper", "whisperX"])
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whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
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choices=[None, "base.en", "small.en", "medium.en", "large"])
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align_model_choice = gr.Radio(label="Forced alignment model", value="whisperX", choices=[None, "whisperX"])
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with gr.Row():
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with gr.Column(scale=2):
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input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath", interactive=True)
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with gr.Group():
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original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript,
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info="Use whisper model to get the transcript. Fix and align it if necessary.")
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with gr.Accordion("Word start time", open=False):
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transcript_with_start_time = gr.Textbox(label="Start time", lines=5, interactive=False, info="Start time before each word")
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with gr.Accordion("Word end time", open=False):
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transcript_with_end_time = gr.Textbox(label="End time", lines=5, interactive=False, info="End time after each word")
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transcribe_btn = gr.Button(value="Transcribe")
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align_btn = gr.Button(value="Align")
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with gr.Column(scale=3):
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with gr.Group():
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transcript = gr.Textbox(label="Text", lines=7, value=demo_text["TTS"]["smart"])
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with gr.Row():
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smart_transcript = gr.Checkbox(label="Smart transcript", value=True)
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with gr.Accordion(label="?", open=False):
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info = gr.Markdown(value=smart_transcript_info)
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with gr.Row():
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mode = gr.Radio(label="Mode", choices=["TTS", "Edit", "Long TTS"], value="TTS")
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split_text = gr.Radio(label="Split text", choices=["Newline", "Sentence"], value="Newline",
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info="Split text into parts and run TTS for each part.", visible=False)
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edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half",
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info="What to do with first and last word", visible=False)
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with gr.Group() as tts_mode_controls:
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prompt_to_word = gr.Dropdown(label="Last word in prompt", choices=demo_words, value=demo_words[10], interactive=True)
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prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.001, value=3.016)
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with gr.Group(visible=False) as edit_mode_controls:
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def get_app():
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with gr.Blocks() as app:
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with gr.Row():
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with gr.Column(scale=2):
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load_models_btn = gr.Button(value="Load models")
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with gr.Column(scale=5):
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with gr.Accordion("Select models", open=False) as models_selector:
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with gr.Row():
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edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, value=demo_words[2], interactive=True)
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edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, value=demo_words[12], interactive=True)
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voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M",
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choices=["giga330M", "giga830M", "giga330M_TTSEnhanced"])
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whisper_backend_choice = gr.Radio(label="Whisper backend", value="whisperX", choices=["whisper", "whisperX"])
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whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
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choices=[None, "base.en", "small.en", "medium.en", "large"])
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align_model_choice = gr.Radio(label="Forced alignment model", value="whisperX", choices=[None, "whisperX"])
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with gr.Row():
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with gr.Column(scale=2):
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input_audio = gr.Audio(value=f"{DEMO_PATH}/84_121550_000074_000000.wav", label="Input Audio", type="filepath", interactive=True)
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with gr.Group():
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original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript,
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info="Use whisper model to get the transcript. Fix and align it if necessary.")
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with gr.Accordion("Word start time", open=False):
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transcript_with_start_time = gr.Textbox(label="Start time", lines=5, interactive=False, info="Start time before each word")
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with gr.Accordion("Word end time", open=False):
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transcript_with_end_time = gr.Textbox(label="End time", lines=5, interactive=False, info="End time after each word")
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transcribe_btn = gr.Button(value="Transcribe")
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align_btn = gr.Button(value="Align")
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with gr.Column(scale=3):
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with gr.Group():
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transcript = gr.Textbox(label="Text", lines=7, value=demo_text["TTS"]["smart"])
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with gr.Row():
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edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=7.93, step=0.001, value=0.46)
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edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=7.93, step=0.001, value=3.808)
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smart_transcript = gr.Checkbox(label="Smart transcript", value=True)
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with gr.Accordion(label="?", open=False):
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info = gr.Markdown(value=smart_transcript_info)
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run_btn = gr.Button(value="Run")
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with gr.Row():
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mode = gr.Radio(label="Mode", choices=["TTS", "Edit", "Long TTS"], value="TTS")
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split_text = gr.Radio(label="Split text", choices=["Newline", "Sentence"], value="Newline",
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info="Split text into parts and run TTS for each part.", visible=False)
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edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half",
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info="What to do with first and last word", visible=False)
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with gr.Column(scale=2):
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output_audio = gr.Audio(label="Output Audio")
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with gr.Accordion("Inference transcript", open=False):
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inference_transcript = gr.Textbox(label="Inference transcript", lines=5, interactive=False,
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info="Inference was performed on this transcript.")
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with gr.Group(visible=False) as long_tts_sentence_editor:
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sentence_selector = gr.Dropdown(label="Sentence", value=None,
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info="Select sentence you want to regenerate")
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sentence_audio = gr.Audio(label="Sentence Audio", scale=2)
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rerun_btn = gr.Button(value="Rerun")
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with gr.Group() as tts_mode_controls:
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prompt_to_word = gr.Dropdown(label="Last word in prompt", choices=demo_words, value=demo_words[10], interactive=True)
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prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.001, value=3.016)
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with gr.Row():
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with gr.Accordion("Generation Parameters - change these if you are unhappy with the generation", open=False):
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stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3, 4], value=3,
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info="if there are long silence in the generated audio, reduce the stop_repetition to 2 or 1. -1 = disabled")
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sample_batch_size = gr.Number(label="speech rate", value=4, precision=0,
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info="The higher the number, the faster the output will be. "
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"Under the hood, the model will generate this many samples and choose the shortest one. "
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"For giga330M_TTSEnhanced, 1 or 2 should be fine since the model is trained to do TTS.")
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seed = gr.Number(label="seed", value=-1, precision=0, info="random seeds always works :)")
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kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
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info="set to 0 to use less VRAM, but with slower inference")
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left_margin = gr.Number(label="left_margin", value=0.08, info="margin to the left of the editing segment")
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right_margin = gr.Number(label="right_margin", value=0.08, info="margin to the right of the editing segment")
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top_p = gr.Number(label="top_p", value=0.9, info="0.9 is a good value, 0.8 is also good")
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temperature = gr.Number(label="temperature", value=1, info="haven't try other values, do not recommend to change")
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top_k = gr.Number(label="top_k", value=0, info="0 means we don't use topk sampling, because we use topp sampling")
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codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000, info='encodec specific, Do not change')
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codec_sr = gr.Number(label="codec_sr", value=50, info='encodec specific, Do not change')
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silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]", info="encodec specific, do not change")
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with gr.Group(visible=False) as edit_mode_controls:
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with gr.Row():
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edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, value=demo_words[2], interactive=True)
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edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, value=demo_words[12], interactive=True)
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with gr.Row():
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edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=7.93, step=0.001, value=0.46)
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edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=7.93, step=0.001, value=3.808)
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run_btn = gr.Button(value="Run")
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with gr.Column(scale=2):
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output_audio = gr.Audio(label="Output Audio")
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with gr.Accordion("Inference transcript", open=False):
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inference_transcript = gr.Textbox(label="Inference transcript", lines=5, interactive=False,
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info="Inference was performed on this transcript.")
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with gr.Group(visible=False) as long_tts_sentence_editor:
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sentence_selector = gr.Dropdown(label="Sentence", value=None,
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info="Select sentence you want to regenerate")
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sentence_audio = gr.Audio(label="Sentence Audio", scale=2)
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rerun_btn = gr.Button(value="Rerun")
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with gr.Row():
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with gr.Accordion("Generation Parameters - change these if you are unhappy with the generation", open=False):
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stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3, 4], value=3,
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info="if there are long silence in the generated audio, reduce the stop_repetition to 2 or 1. -1 = disabled")
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sample_batch_size = gr.Number(label="speech rate", value=4, precision=0,
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info="The higher the number, the faster the output will be. "
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"Under the hood, the model will generate this many samples and choose the shortest one. "
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"For giga330M_TTSEnhanced, 1 or 2 should be fine since the model is trained to do TTS.")
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seed = gr.Number(label="seed", value=-1, precision=0, info="random seeds always works :)")
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kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
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info="set to 0 to use less VRAM, but with slower inference")
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left_margin = gr.Number(label="left_margin", value=0.08, info="margin to the left of the editing segment")
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right_margin = gr.Number(label="right_margin", value=0.08, info="margin to the right of the editing segment")
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top_p = gr.Number(label="top_p", value=0.9, info="0.9 is a good value, 0.8 is also good")
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temperature = gr.Number(label="temperature", value=1, info="haven't try other values, do not recommend to change")
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top_k = gr.Number(label="top_k", value=0, info="0 means we don't use topk sampling, because we use topp sampling")
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codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000, info='encodec specific, Do not change')
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codec_sr = gr.Number(label="codec_sr", value=50, info='encodec specific, Do not change')
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silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]", info="encodec specific, do not change")
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audio_tensors = gr.State()
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transcribe_state = gr.State(value={"words_info": demo_words_info})
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audio_tensors = gr.State()
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transcribe_state = gr.State(value={"words_info": demo_words_info})
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mode.change(fn=update_demo,
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inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
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outputs=[transcript, edit_from_word, edit_to_word])
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edit_word_mode.change(fn=update_demo,
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inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
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outputs=[transcript, edit_from_word, edit_to_word])
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smart_transcript.change(fn=update_demo,
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mode.change(fn=update_demo,
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inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
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outputs=[transcript, edit_from_word, edit_to_word])
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edit_word_mode.change(fn=update_demo,
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inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
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outputs=[transcript, edit_from_word, edit_to_word])
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smart_transcript.change(fn=update_demo,
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inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
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outputs=[transcript, edit_from_word, edit_to_word])
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load_models_btn.click(fn=load_models,
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inputs=[whisper_backend_choice, whisper_model_choice, align_model_choice, voicecraft_model_choice],
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outputs=[models_selector])
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load_models_btn.click(fn=load_models,
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inputs=[whisper_backend_choice, whisper_model_choice, align_model_choice, voicecraft_model_choice],
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outputs=[models_selector])
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input_audio.upload(fn=update_input_audio,
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inputs=[input_audio],
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outputs=[prompt_end_time, edit_start_time, edit_end_time])
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transcribe_btn.click(fn=transcribe,
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inputs=[seed, input_audio],
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outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time,
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prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
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align_btn.click(fn=align,
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inputs=[seed, original_transcript, input_audio],
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outputs=[transcript_with_start_time, transcript_with_end_time,
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prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
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input_audio.upload(fn=update_input_audio,
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inputs=[input_audio],
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outputs=[prompt_end_time, edit_start_time, edit_end_time])
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transcribe_btn.click(fn=transcribe,
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inputs=[seed, input_audio],
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outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time,
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prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
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align_btn.click(fn=align,
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inputs=[seed, original_transcript, input_audio],
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outputs=[transcript_with_start_time, transcript_with_end_time,
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prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
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mode.change(fn=change_mode,
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inputs=[mode],
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outputs=[tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor])
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mode.change(fn=change_mode,
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inputs=[mode],
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outputs=[tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor])
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run_btn.click(fn=run,
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inputs=[
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seed, left_margin, right_margin,
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codec_audio_sr, codec_sr,
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top_k, top_p, temperature,
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stop_repetition, sample_batch_size,
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kvcache, silence_tokens,
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input_audio, transcribe_state, transcript, smart_transcript,
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mode, prompt_end_time, edit_start_time, edit_end_time,
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split_text, sentence_selector, audio_tensors
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],
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outputs=[output_audio, inference_transcript, sentence_selector, audio_tensors])
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sentence_selector.change(fn=load_sentence,
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inputs=[sentence_selector, codec_audio_sr, audio_tensors],
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outputs=[sentence_audio])
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rerun_btn.click(fn=run,
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run_btn.click(fn=run,
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inputs=[
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seed, left_margin, right_margin,
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codec_audio_sr, codec_sr,
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@@ -578,24 +561,58 @@ with gr.Blocks() as app:
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stop_repetition, sample_batch_size,
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kvcache, silence_tokens,
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input_audio, transcribe_state, transcript, smart_transcript,
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gr.State(value="Rerun"), prompt_end_time, edit_start_time, edit_end_time,
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mode, prompt_end_time, edit_start_time, edit_end_time,
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split_text, sentence_selector, audio_tensors
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],
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outputs=[output_audio, inference_transcript, sentence_audio, audio_tensors])
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outputs=[output_audio, inference_transcript, sentence_selector, audio_tensors])
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prompt_to_word.change(fn=update_bound_word,
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inputs=[gr.State(False), prompt_to_word, gr.State("Replace all")],
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outputs=[prompt_end_time])
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edit_from_word.change(fn=update_bound_word,
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inputs=[gr.State(True), edit_from_word, edit_word_mode],
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outputs=[edit_start_time])
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edit_to_word.change(fn=update_bound_word,
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inputs=[gr.State(False), edit_to_word, edit_word_mode],
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outputs=[edit_end_time])
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edit_word_mode.change(fn=update_bound_words,
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inputs=[edit_from_word, edit_to_word, edit_word_mode],
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outputs=[edit_start_time, edit_end_time])
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sentence_selector.change(fn=load_sentence,
|
||||
inputs=[sentence_selector, codec_audio_sr, audio_tensors],
|
||||
outputs=[sentence_audio])
|
||||
rerun_btn.click(fn=run,
|
||||
inputs=[
|
||||
seed, left_margin, right_margin,
|
||||
codec_audio_sr, codec_sr,
|
||||
top_k, top_p, temperature,
|
||||
stop_repetition, sample_batch_size,
|
||||
kvcache, silence_tokens,
|
||||
input_audio, transcribe_state, transcript, smart_transcript,
|
||||
gr.State(value="Rerun"), prompt_end_time, edit_start_time, edit_end_time,
|
||||
split_text, sentence_selector, audio_tensors
|
||||
],
|
||||
outputs=[output_audio, inference_transcript, sentence_audio, audio_tensors])
|
||||
|
||||
prompt_to_word.change(fn=update_bound_word,
|
||||
inputs=[gr.State(False), prompt_to_word, gr.State("Replace all")],
|
||||
outputs=[prompt_end_time])
|
||||
edit_from_word.change(fn=update_bound_word,
|
||||
inputs=[gr.State(True), edit_from_word, edit_word_mode],
|
||||
outputs=[edit_start_time])
|
||||
edit_to_word.change(fn=update_bound_word,
|
||||
inputs=[gr.State(False), edit_to_word, edit_word_mode],
|
||||
outputs=[edit_end_time])
|
||||
edit_word_mode.change(fn=update_bound_words,
|
||||
inputs=[edit_from_word, edit_to_word, edit_word_mode],
|
||||
outputs=[edit_start_time, edit_end_time])
|
||||
return app
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.launch(share=True)
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(description="VoiceCraft gradio app.")
|
||||
|
||||
parser.add_argument("--demo-path", default=".demo", help="Path to demo directory")
|
||||
parser.add_argument("--tmp-path", default=".demo/temp", help="Path to tmp directory")
|
||||
parser.add_argument("--models-path", default=".pretrained_models", help="Path to voicecraft models directory")
|
||||
parser.add_argument("--port", default=7860, type=int, help="App port")
|
||||
parser.add_argument("--share", action="store_true", help="Launch with public url")
|
||||
|
||||
os.environ["USER"] = os.getenv("USER", "user")
|
||||
args = parser.parse_args()
|
||||
DEMO_PATH = args.demo_path
|
||||
TMP_PATH = args.tmp_path
|
||||
MODELS_PATH = args.models_path
|
||||
|
||||
app = get_app()
|
||||
app.launch(share=args.share, server_port=args.port)
|
||||
|
||||
Reference in New Issue
Block a user