forked from mrq/tortoise-tts
304 lines
12 KiB
Python
Executable File
304 lines
12 KiB
Python
Executable File
import os
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import argparse
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import gradio as gr
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import torch
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import torchaudio
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import time
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import json
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from datetime import datetime
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from tortoise.api import TextToSpeech
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from tortoise.utils.audio import load_audio, load_voice, load_voices
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from tortoise.utils.text import split_and_recombine_text
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import music_tag
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def generate(text, delimiter, emotion, prompt, voice, mic_audio, preset, seed, candidates, num_autoregressive_samples, diffusion_iterations, temperature, diffusion_sampler, breathing_room, experimentals, progress=gr.Progress()):
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if voice != "microphone":
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voices = [voice]
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else:
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voices = []
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if voice == "microphone":
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if mic_audio is None:
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raise gr.Error("Please provide audio from mic when choosing `microphone` as a voice input")
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mic = load_audio(mic_audio, 22050)
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voice_samples, conditioning_latents = [mic], None
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else:
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progress(0, desc="Loading voice...")
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voice_samples, conditioning_latents = load_voice(voice)
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if voice_samples is not None:
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sample_voice = voice_samples[0]
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conditioning_latents = tts.get_conditioning_latents(voice_samples, progress=progress, max_chunk_size=args.cond_latent_max_chunk_size)
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torch.save(conditioning_latents, os.path.join(f'./tortoise/voices/{voice}/', f'cond_latents.pth'))
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voice_samples = None
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else:
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sample_voice = None
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if seed == 0:
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seed = None
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start_time = time.time()
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settings = {
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'temperature': temperature, 'length_penalty': 1.0, 'repetition_penalty': 2.0,
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'top_p': .8,
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'cond_free_k': 2.0, 'diffusion_temperature': 1.0,
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'num_autoregressive_samples': num_autoregressive_samples,
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'diffusion_iterations': diffusion_iterations,
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'voice_samples': voice_samples,
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'conditioning_latents': conditioning_latents,
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'use_deterministic_seed': seed,
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'return_deterministic_state': True,
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'k': candidates,
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'diffusion_sampler': diffusion_sampler,
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'breathing_room': breathing_room,
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'progress': progress,
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'half_p': "Half Precision" in experimentals,
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'cond_free': "Conditioning-Free" in experimentals,
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}
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if delimiter == "\\n":
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delimiter = "\n"
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if delimiter != "" and delimiter in text:
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texts = text.split(delimiter)
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else:
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texts = split_and_recombine_text(text)
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timestamp = int(time.time())
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outdir = f"./results/{voice}/{timestamp}/"
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os.makedirs(outdir, exist_ok=True)
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audio_cache = {}
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for line, cut_text in enumerate(texts):
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if emotion == "Custom" and prompt.strip() != "":
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cut_text = f"[{prompt},] {cut_text}"
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elif emotion != "None":
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cut_text = f"[I am really {emotion.lower()},] {cut_text}"
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print(f"[{str(line+1)}/{str(len(texts))}] Generating line: {cut_text}")
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gen, additionals = tts.tts(cut_text, **settings )
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seed = additionals[0]
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if isinstance(gen, list):
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for j, g in enumerate(gen):
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audio = g.squeeze(0).cpu()
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audio_cache[f"candidate_{j}/result_{line}.wav"] = {
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'audio': audio,
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'text': cut_text,
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}
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os.makedirs(os.path.join(outdir, f'candidate_{j}'), exist_ok=True)
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torchaudio.save(os.path.join(outdir, f'candidate_{j}/result_{line}.wav'), audio, 24000)
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else:
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audio = gen.squeeze(0).cpu()
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audio_cache[f"result_{line}.wav"] = {
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'audio': audio,
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'text': cut_text,
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}
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torchaudio.save(os.path.join(outdir, f'result_{line}.wav'), audio, 24000)
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output_voice = None
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if len(texts) > 1:
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for candidate in range(candidates):
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audio_clips = []
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for line in range(len(texts)):
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if isinstance(gen, list):
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audio = audio_cache[f'candidate_{candidate}/result_{line}.wav']['audio']
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else:
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audio = audio_cache[f'result_{line}.wav']['audio']
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audio_clips.append(audio)
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audio_clips = torch.cat(audio_clips, dim=-1)
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torchaudio.save(os.path.join(outdir, f'combined_{candidate}.wav'), audio_clips, 24000)
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if output_voice is None:
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output_voice = (24000, audio_clips.squeeze().cpu().numpy())
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else:
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if isinstance(gen, list):
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output_voice = gen[0]
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else:
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output_voice = gen
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output_voice = (24000, output_voice.squeeze().cpu().numpy())
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info = {
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'text': text,
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'delimiter': delimiter,
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'emotion': emotion,
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'prompt': prompt,
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'voice': voice,
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'mic_audio': mic_audio,
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'preset': preset,
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'seed': seed,
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'candidates': candidates,
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'num_autoregressive_samples': num_autoregressive_samples,
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'diffusion_iterations': diffusion_iterations,
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'temperature': temperature,
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'diffusion_sampler': diffusion_sampler,
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'breathing_room': breathing_room,
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'experimentals': experimentals,
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'time': time.time()-start_time,
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}
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with open(os.path.join(outdir, f'input.txt'), 'w', encoding="utf-8") as f:
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f.write(json.dumps(info, indent='\t') )
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print(f"Saved to '{outdir}'")
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for path in audio_cache:
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info['text'] = audio_cache[path]['text']
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metadata = music_tag.load_file(os.path.join(outdir, path))
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metadata['lyrics'] = json.dumps(info)
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metadata.save()
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if sample_voice is not None:
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sample_voice = (22050, sample_voice.squeeze().cpu().numpy())
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audio_clips = []
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return (
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sample_voice,
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output_voice,
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seed
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)
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def update_presets(value):
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PRESETS = {
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'Ultra Fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 30, 'cond_free': False},
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'Fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 80},
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'Standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 200},
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'High Quality': {'num_autoregressive_samples': 256, 'diffusion_iterations': 400},
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}
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if value in PRESETS:
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preset = PRESETS[value]
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return (gr.update(value=preset['num_autoregressive_samples']), gr.update(value=preset['diffusion_iterations']))
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else:
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return (gr.update(), gr.update())
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def update_voices():
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return gr.Dropdown.update(choices=os.listdir(os.path.join("tortoise", "voices")) + ["microphone"])
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def main():
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with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column():
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text = gr.Textbox(lines=4, label="Prompt")
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delimiter = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
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emotion = gr.Radio(
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["None", "Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom"],
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value="None",
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label="Emotion",
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type="value",
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interactive=True
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)
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prompt = gr.Textbox(lines=1, label="Custom Emotion + Prompt (if selected)")
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voice = gr.Dropdown(
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os.listdir(os.path.join("tortoise", "voices")) + ["microphone"],
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label="Voice",
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type="value",
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)
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mic_audio = gr.Audio(
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label="Microphone Source",
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source="microphone",
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type="filepath",
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)
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refresh_voices = gr.Button(value="Refresh Voice List")
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refresh_voices.click(update_voices,
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inputs=None,
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outputs=voice
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)
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prompt.change(fn=lambda value: gr.update(value="Custom"),
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inputs=prompt,
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outputs=emotion
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)
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mic_audio.change(fn=lambda value: gr.update(value="microphone"),
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inputs=mic_audio,
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outputs=voice
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)
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with gr.Column():
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candidates = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates")
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seed = gr.Number(value=0, precision=0, label="Seed")
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preset = gr.Radio(
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["Ultra Fast", "Fast", "Standard", "High Quality", "None"],
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value="None",
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label="Preset",
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type="value",
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)
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num_autoregressive_samples = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Samples")
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diffusion_iterations = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations")
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temperature = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
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breathing_room = gr.Slider(value=12, minimum=1, maximum=32, step=1, label="Pause Size")
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diffusion_sampler = gr.Radio(
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["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"],
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value="P",
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label="Diffusion Samplers",
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type="value",
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)
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experimentals = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags")
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preset.change(fn=update_presets,
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inputs=preset,
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outputs=[
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num_autoregressive_samples,
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diffusion_iterations,
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],
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)
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with gr.Column():
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selected_voice = gr.Audio(label="Source Sample")
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output_audio = gr.Audio(label="Output")
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usedSeed = gr.Textbox(label="Seed", placeholder="0", interactive=False)
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submit = gr.Button(value="Generate")
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#stop = gr.Button(value="Stop")
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submit_event = submit.click(generate,
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inputs=[
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text,
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delimiter,
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emotion,
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prompt,
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voice,
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mic_audio,
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preset,
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seed,
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candidates,
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num_autoregressive_samples,
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diffusion_iterations,
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temperature,
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diffusion_sampler,
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breathing_room,
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experimentals,
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],
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outputs=[selected_voice, output_audio, usedSeed],
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)
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#stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_event])
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demo.queue().launch(share=args.share)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--share", action='store_true', help="Lets Gradio return a public URL to use anywhere")
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parser.add_argument("--low-vram", action='store_true', help="Disables some optimizations that increases VRAM usage")
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parser.add_argument("--cond-latent-max-chunk-size", type=int, default=1000000, help="Sets an upper limit to audio chunk size when computing conditioning latents")
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args = parser.parse_args()
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tts = TextToSpeech(minor_optimizations=not args.low_vram)
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main() |