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https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-21 23:57:58 +01:00
Better TTS with autoplay
- Adds "still_streaming" to shared module for extensions to know if generation is complete - Changed TTS extension with new options: - Show text under the audio widget - Automatically play the audio once text generation finishes - manage the generated wav files (only keep files for finished generations, optional max file limit) - [wip] ability to change voice pitch and speed - added 'tensorboard' to requirements, since python sent "tensorboard not found" errors after a fresh installation.
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Xan
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c93f1fa99b
commit
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@ -4,3 +4,4 @@ pydub
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PyYAML
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torch
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torchaudio
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simpleaudio
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@ -4,20 +4,36 @@ from pathlib import Path
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import gradio as gr
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import torch
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import modules.shared as shared
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import simpleaudio as sa
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torch._C._jit_set_profiling_mode(False)
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params = {
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'activate': True,
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'speaker': 'en_56',
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'speaker': 'en_5',
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'language': 'en',
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'model_id': 'v3_en',
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'sample_rate': 48000,
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'device': 'cpu',
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'max_wavs': 20,
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'play_audio': True,
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'show_text': True,
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}
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current_params = params.copy()
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voices_by_gender = ['en_99', 'en_45', 'en_18', 'en_117', 'en_49', 'en_51', 'en_68', 'en_0', 'en_26', 'en_56', 'en_74', 'en_5', 'en_38', 'en_53', 'en_21', 'en_37', 'en_107', 'en_10', 'en_82', 'en_16', 'en_41', 'en_12', 'en_67', 'en_61', 'en_14', 'en_11', 'en_39', 'en_52', 'en_24', 'en_97', 'en_28', 'en_72', 'en_94', 'en_36', 'en_4', 'en_43', 'en_88', 'en_25', 'en_65', 'en_6', 'en_44', 'en_75', 'en_91', 'en_60', 'en_109', 'en_85', 'en_101', 'en_108', 'en_50', 'en_96', 'en_64', 'en_92', 'en_76', 'en_33', 'en_116', 'en_48', 'en_98', 'en_86', 'en_62', 'en_54', 'en_95', 'en_55', 'en_111', 'en_3', 'en_83', 'en_8', 'en_47', 'en_59', 'en_1', 'en_2', 'en_7', 'en_9', 'en_13', 'en_15', 'en_17', 'en_19', 'en_20', 'en_22', 'en_23', 'en_27', 'en_29', 'en_30', 'en_31', 'en_32', 'en_34', 'en_35', 'en_40', 'en_42', 'en_46', 'en_57', 'en_58', 'en_63', 'en_66', 'en_69', 'en_70', 'en_71', 'en_73', 'en_77', 'en_78', 'en_79', 'en_80', 'en_81', 'en_84', 'en_87', 'en_89', 'en_90', 'en_93', 'en_100', 'en_102', 'en_103', 'en_104', 'en_105', 'en_106', 'en_110', 'en_112', 'en_113', 'en_114', 'en_115']
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wav_idx = 0
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table = str.maketrans({
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"<": "<",
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">": ">",
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"&": "&",
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"'": "'",
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'"': """,
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})
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def xmlesc(txt):
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return txt.translate(table)
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def load_model():
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model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language=params['language'], speaker=params['model_id'])
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model.to(params['device'])
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@ -58,20 +74,45 @@ def output_modifier(string):
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if params['activate'] == False:
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return string
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orig_string = string
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string = remove_surrounded_chars(string)
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string = string.replace('"', '')
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string = string.replace('“', '')
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string = string.replace('\n', ' ')
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string = string.strip()
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auto_playable=True
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if string == '':
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string = 'empty reply, try regenerating'
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string = 'empty reply, try regenerating'
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auto_playable=False
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#x-slow, slow, medium, fast, x-fast
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#x-low, low, medium, high, x-high
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#prosody='<prosody rate="fast" pitch="medium">'
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prosody='<prosody rate="fast">'
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string ='<speak>'+prosody+xmlesc(string)+'</prosody></speak>'
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output_file = Path(f'extensions/silero_tts/outputs/{wav_idx:06d}.wav')
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audio = model.save_wav(text=string, speaker=params['speaker'], sample_rate=int(params['sample_rate']), audio_path=str(output_file))
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audio = model.save_wav(ssml_text=string, speaker=params['speaker'], sample_rate=int(params['sample_rate']), audio_path=str(output_file))
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string = f'<audio src="file/{output_file.as_posix()}" controls></audio>'
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wav_idx += 1
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#reset if too many wavs. set max to -1 for unlimited.
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if wav_idx < params['max_wavs'] and params['max_wavs'] > 0:
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#only increment if starting a new stream, else replace during streaming. Does not update duration on webui sometimes?
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if not shared.still_streaming:
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wav_idx += 1
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else:
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wav_idx = 0
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if params['show_text']:
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string+='\n\n'+orig_string
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#if params['play_audio'] == True and auto_playable and shared.stop_everything:
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if params['play_audio'] == True and auto_playable and not shared.still_streaming:
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stop_autoplay()
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wave_obj = sa.WaveObject.from_wave_file(output_file.as_posix())
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wave_obj.play()
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return string
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@ -84,11 +125,20 @@ def bot_prefix_modifier(string):
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return string
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def stop_autoplay():
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sa.stop_all()
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def ui():
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# Gradio elements
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activate = gr.Checkbox(value=params['activate'], label='Activate TTS')
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show_text = gr.Checkbox(value=params['show_text'], label='Show message text under audio player')
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play_audio = gr.Checkbox(value=params['play_audio'], label='Play TTS automatically')
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stop_audio = gr.Button("Stop Auto-Play")
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voice = gr.Dropdown(value=params['speaker'], choices=voices_by_gender, label='TTS voice')
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# Event functions to update the parameters in the backend
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activate.change(lambda x: params.update({"activate": x}), activate, None)
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play_audio.change(lambda x: params.update({"play_audio": x}), play_audio, None)
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show_text.change(lambda x: params.update({"show_text": x}), show_text, None)
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stop_audio.click(stop_autoplay)
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voice.change(lambda x: params.update({"speaker": x}), voice, None)
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@ -12,6 +12,7 @@ is_LLaMA = False
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history = {'internal': [], 'visible': []}
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character = 'None'
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stop_everything = False
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still_streaming = False
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# UI elements (buttons, sliders, HTML, etc)
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gradio = {}
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@ -182,6 +182,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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# Generate the reply 8 tokens at a time
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else:
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yield formatted_outputs(original_question, shared.model_name)
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shared.still_streaming = True
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for i in tqdm(range(max_new_tokens//8+1)):
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with torch.no_grad():
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output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
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@ -191,8 +192,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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reply = decode(output)
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if not (shared.args.chat or shared.args.cai_chat):
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reply = original_question + apply_extensions(reply[len(question):], "output")
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yield formatted_outputs(reply, shared.model_name)
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if not shared.args.flexgen:
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if output[-1] == n:
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break
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@ -201,6 +201,13 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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if np.count_nonzero(input_ids[0] == n) < np.count_nonzero(output == n):
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break
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input_ids = np.reshape(output, (1, output.shape[0]))
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#Mid-stream yield, ran if no breaks
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yield formatted_outputs(reply, shared.model_name)
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if shared.soft_prompt:
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inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
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#Stream finished from max tokens or break. Do final yield.
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shared.still_streaming = False
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yield formatted_outputs(reply, shared.model_name)
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@ -6,3 +6,4 @@ numpy
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rwkv==0.0.6
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safetensors==0.2.8
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git+https://github.com/huggingface/transformers
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tensorboard
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