new TTS model, better prompt
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pyp_l40
parent
13e52470c3
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And again in two thousand and eight when the United States Central Bank, the Federal Reserve, printed over two trillion dollars.
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And again in two thousand and eight when the United States Central Bank, the Federal Reserve, printed over two trillion dollars.
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@ -77,8 +77,15 @@ class WhisperxModel:
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def load_models(whisper_backend_name, whisper_model_name, alignment_model_name, voicecraft_model_name):
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global transcribe_model, align_model, voicecraft_model
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if voicecraft_model_name == "giga330M_TTSEnhanced":
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if voicecraft_model_name == "330M":
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voicecraft_model_name = "giga330M"
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elif voicecraft_model_name == "830M":
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voicecraft_model_name = "giga830M"
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elif voicecraft_model_name == "330M_TTSEnhanced":
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voicecraft_model_name = "gigaHalfLibri330M_TTSEnhanced_max16s"
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elif voicecraft_model_name == "830M_TTSEnhanced":
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voicecraft_model_name = "830M_TTSEnhanced"
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if alignment_model_name is not None:
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align_model = WhisperxAlignModel()
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@ -132,7 +139,7 @@ def transcribe(seed, audio_path):
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segments = transcribe_model.transcribe(audio_path)
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state = get_transcribe_state(segments)
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print(state)
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return [
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state["transcript"], state["transcript_with_start_time"], state["transcript_with_end_time"],
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gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # prompt_to_word
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@ -364,50 +371,32 @@ If disabled, you should write the target transcript yourself:</br>
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- In Edit mode write full prompt</br>
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"""
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demo_original_transcript = "But when I had approached so near to them, the common object, which the sense deceives, lost not by distance any of its marks."
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demo_original_transcript = "Gwynplaine had, besides, for his work and for his feats of strength, round his neck and over his shoulders, an esclavine of leather."
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demo_text = {
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"TTS": {
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"smart": "I cannot believe that the same model can also do text to speech synthesis too!",
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"regular": "But when I had approached so near to them, the common I cannot believe that the same model can also do text to speech synthesis too!"
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"regular": "Gwynplaine had, besides, for his work and for his feats of strength, I cannot believe that the same model can also do text to speech synthesis too!"
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},
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"Edit": {
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"smart": "saw the mirage of the lake in the distance,",
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"regular": "But when I saw the mirage of the lake in the distance, which the sense deceives, Lost not by distance any of its marks,"
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"smart": "take over the stage for half an hour,",
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"regular": "Gwynplaine had, besides, for his work and for his feats of strength, take over the stage for half an hour, an esclavine of leather."
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},
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"Long TTS": {
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"smart": "You can run the model on a big text!\n"
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"Just write it line-by-line. Or sentence-by-sentence.\n"
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"If some sentences sound odd, just rerun the model on them, no need to generate the whole text again!",
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"regular": "But when I had approached so near to them, the common You can run the model on a big text!\n"
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"But when I had approached so near to them, the common Just write it line-by-line. Or sentence-by-sentence.\n"
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"But when I had approached so near to them, the common If some sentences sound odd, just rerun the model on them, no need to generate the whole text again!"
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"regular": "Gwynplaine had, besides, for his work and for his feats of strength, You can run the model on a big text!\n"
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"Gwynplaine had, besides, for his work and for his feats of strength, Just write it line-by-line. Or sentence-by-sentence.\n"
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"Gwynplaine had, besides, for his work and for his feats of strength, If some sentences sound odd, just rerun the model on them, no need to generate the whole text again!"
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}
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}
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all_demo_texts = {vv for k, v in demo_text.items() for kk, vv in v.items()}
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demo_words = [
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'0.029 But 0.149', '0.189 when 0.33', '0.43 I 0.49', '0.53 had 0.65', '0.711 approached 1.152', '1.352 so 1.593',
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'1.693 near 1.933', '1.994 to 2.074', '2.134 them, 2.354', '2.535 the 2.655', '2.695 common 3.016', '3.196 object, 3.577',
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'3.717 which 3.898', '3.958 the 4.058', '4.098 sense 4.359', '4.419 deceives, 4.92', '5.101 lost 5.481', '5.682 not 5.963',
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'6.043 by 6.183', '6.223 distance 6.644', '6.905 any 7.065', '7.125 of 7.185', '7.245 its 7.346', '7.406 marks. 7.727'
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]
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demo_words = ['0.069 Gwynplain 0.611', '0.671 had, 0.912', '0.952 besides, 1.414', '1.494 for 1.634', '1.695 his 1.835', '1.915 work 2.136', '2.196 and 2.297', '2.337 for 2.517', '2.557 his 2.678', '2.758 feats 3.019', '3.079 of 3.139', '3.2 strength, 3.561', '4.022 round 4.263', '4.303 his 4.444', '4.524 neck 4.705', '4.745 and 4.825', '4.905 over 5.086', '5.146 his 5.266', '5.307 shoulders, 5.768', '6.23 an 6.33', '6.531 esclavine 7.133', '7.213 of 7.293', '7.353 leather. 7.614']
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demo_words_info = [
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{'word': 'But', 'start': 0.029, 'end': 0.149, 'score': 0.834}, {'word': 'when', 'start': 0.189, 'end': 0.33, 'score': 0.879},
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{'word': 'I', 'start': 0.43, 'end': 0.49, 'score': 0.984}, {'word': 'had', 'start': 0.53, 'end': 0.65, 'score': 0.998},
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{'word': 'approached', 'start': 0.711, 'end': 1.152, 'score': 0.822}, {'word': 'so', 'start': 1.352, 'end': 1.593, 'score': 0.822},
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{'word': 'near', 'start': 1.693, 'end': 1.933, 'score': 0.752}, {'word': 'to', 'start': 1.994, 'end': 2.074, 'score': 0.924},
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{'word': 'them,', 'start': 2.134, 'end': 2.354, 'score': 0.914}, {'word': 'the', 'start': 2.535, 'end': 2.655, 'score': 0.818},
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{'word': 'common', 'start': 2.695, 'end': 3.016, 'score': 0.971}, {'word': 'object,', 'start': 3.196, 'end': 3.577, 'score': 0.823},
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{'word': 'which', 'start': 3.717, 'end': 3.898, 'score': 0.701}, {'word': 'the', 'start': 3.958, 'end': 4.058, 'score': 0.798},
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{'word': 'sense', 'start': 4.098, 'end': 4.359, 'score': 0.797}, {'word': 'deceives,', 'start': 4.419, 'end': 4.92, 'score': 0.802},
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{'word': 'lost', 'start': 5.101, 'end': 5.481, 'score': 0.71}, {'word': 'not', 'start': 5.682, 'end': 5.963, 'score': 0.781},
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{'word': 'by', 'start': 6.043, 'end': 6.183, 'score': 0.834}, {'word': 'distance', 'start': 6.223, 'end': 6.644, 'score': 0.899},
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{'word': 'any', 'start': 6.905, 'end': 7.065, 'score': 0.893}, {'word': 'of', 'start': 7.125, 'end': 7.185, 'score': 0.772},
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{'word': 'its', 'start': 7.245, 'end': 7.346, 'score': 0.778}, {'word': 'marks.', 'start': 7.406, 'end': 7.727, 'score': 0.955}
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]
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demo_words_info = [{'word': 'Gwynplain', 'start': 0.069, 'end': 0.611, 'score': 0.833}, {'word': 'had,', 'start': 0.671, 'end': 0.912, 'score': 0.879}, {'word': 'besides,', 'start': 0.952, 'end': 1.414, 'score': 0.863}, {'word': 'for', 'start': 1.494, 'end': 1.634, 'score': 0.89}, {'word': 'his', 'start': 1.695, 'end': 1.835, 'score': 0.669}, {'word': 'work', 'start': 1.915, 'end': 2.136, 'score': 0.916}, {'word': 'and', 'start': 2.196, 'end': 2.297, 'score': 0.766}, {'word': 'for', 'start': 2.337, 'end': 2.517, 'score': 0.808}, {'word': 'his', 'start': 2.557, 'end': 2.678, 'score': 0.786}, {'word': 'feats', 'start': 2.758, 'end': 3.019, 'score': 0.97}, {'word': 'of', 'start': 3.079, 'end': 3.139, 'score': 0.752}, {'word': 'strength,', 'start': 3.2, 'end': 3.561, 'score': 0.742}, {'word': 'round', 'start': 4.022, 'end': 4.263, 'score': 0.916}, {'word': 'his', 'start': 4.303, 'end': 4.444, 'score': 0.666}, {'word': 'neck', 'start': 4.524, 'end': 4.705, 'score': 0.908}, {'word': 'and', 'start': 4.745, 'end': 4.825, 'score': 0.882}, {'word': 'over', 'start': 4.905, 'end': 5.086, 'score': 0.847}, {'word': 'his', 'start': 5.146, 'end': 5.266, 'score': 0.791}, {'word': 'shoulders,', 'start': 5.307, 'end': 5.768, 'score': 0.729}, {'word': 'an', 'start': 6.23, 'end': 6.33, 'score': 0.854}, {'word': 'esclavine', 'start': 6.531, 'end': 7.133, 'score': 0.803}, {'word': 'of', 'start': 7.213, 'end': 7.293, 'score': 0.772}, {'word': 'leather.', 'start': 7.353, 'end': 7.614, 'score': 0.896}]
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def update_demo(mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word):
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@ -434,16 +423,16 @@ def get_app():
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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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voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="830M_TTSEnhanced",
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choices=["330M", "830M", "330M_TTSEnhanced", "830M_TTSEnhanced"])
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whisper_backend_choice = gr.Radio(label="Whisper backend", value="whisperX", choices=["whisperX", "whisper"])
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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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align_model_choice = gr.Radio(label="Forced alignment model", value="whisperX", choices=["whisperX", None])
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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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input_audio = gr.Audio(value=f"{DEMO_PATH}/5895_34622_000026_000002.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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@ -467,20 +456,20 @@ def get_app():
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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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edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace all",
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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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prompt_to_word = gr.Dropdown(label="Last word in prompt", choices=demo_words, value=demo_words[11], interactive=True)
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prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.614, step=0.001, value=3.600)
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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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edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, value=demo_words[12], interactive=True)
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edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, value=demo_words[18], 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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edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=7.614, step=0.001, value=4.022)
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edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=7.614, step=0.001, value=5.768)
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run_btn = gr.Button(value="Run")
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@ -499,7 +488,7 @@ def get_app():
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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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sample_batch_size = gr.Number(label="speech rate", value=3, 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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@ -71,7 +71,7 @@
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"# load model, encodec, and phn2num\n",
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"# # load model, tokenizer, and other necessary files\n",
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"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
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"voicecraft_name=\"giga330M.pth\" # or gigaHalfLibri330M_TTSEnhanced_max16s.pth, giga830M.pth\n",
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"voicecraft_name=\"830M_TTSEnhanced.pth\" # or giga330M.pth, gigaHalfLibri330M_TTSEnhanced_max16s.pth, giga830M.pth\n",
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"\n",
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"# the new way of loading the model, with huggingface, recommended\n",
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"from models import voicecraft\n",
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@ -111,8 +111,8 @@
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"# Prepare your audio\n",
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"# point to the original audio whose speech you want to clone\n",
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"# write down the transcript for the file, or run whisper to get the transcript (and you can modify it if it's not accurate), save it as a .txt file\n",
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"orig_audio = \"./demo/84_121550_000074_000000.wav\"\n",
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"orig_transcript = \"But when I had approached so near to them The common object, which the sense deceives, Lost not by distance any of its marks,\"\n",
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"orig_audio = \"./demo/5895_34622_000026_000002.wav\"\n",
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"orig_transcript = \"Gwynplaine had, besides, for his work and for his feats of strength, round his neck and over his shoulders, an esclavine of leather.\"\n",
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"\n",
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"# move the audio and transcript to temp folder\n",
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"temp_folder = \"./demo/temp\"\n",
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@ -143,8 +143,8 @@
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"outputs": [],
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"source": [
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"# take a look at demo/temp/mfa_alignment, decide which part of the audio to use as prompt\n",
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"cut_off_sec = 3.01 # NOTE: according to forced-alignment file demo/temp/mfa_alignments/84_121550_000074_000000.csv, the word \"common\" stop as 3.01 sec, this should be different for different audio\n",
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"target_transcript = \"But when I had approached so near to them The common I cannot believe that the same model can also do text to speech synthesis as well!\"\n",
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"cut_off_sec = 3.6 # NOTE: according to forced-alignment file demo/temp/mfa_alignments/5895_34622_000026_000002.wav, the word \"strength\" stop as 3.561 sec, so we use first 3.6 sec as the prompt. this should be different for different audio\n",
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"target_transcript = \"Gwynplaine had, besides, for his work and for his feats of strength, I cannot believe that the same model can also do text to speech synthesis too!\"\n",
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"# NOTE: 3 sec of reference is generally enough for high quality voice cloning, but longer is generally better, try e.g. 3~6 sec.\n",
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"audio_fn = f\"{temp_folder}/{filename}.wav\"\n",
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"info = torchaudio.info(audio_fn)\n",
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@ -165,7 +165,7 @@
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"\n",
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"# NOTE adjust the below three arguments if the generation is not as good\n",
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"stop_repetition = 3 # NOTE if the model generate long silence, reduce the stop_repetition to 3, 2 or even 1\n",
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"sample_batch_size = 5 # NOTE: if the if there are long silence or unnaturally strecthed words, increase sample_batch_size to 5 or higher. What this will do to the model is that the model will run sample_batch_size examples of the same audio, and pick the one that's the shortest. So if the speech rate of the generated is too fast change it to a smaller number.\n",
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"sample_batch_size = 3 # NOTE: if the if there are long silence or unnaturally strecthed words, increase sample_batch_size to 4 or higher. What this will do to the model is that the model will run sample_batch_size examples of the same audio, and pick the one that's the shortest. So if the speech rate of the generated is too fast change it to a smaller number.\n",
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"seed = 1 # change seed if you are still unhappy with the result\n",
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"\n",
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"def seed_everything(seed):\n",
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