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