import os
import argparse
import gradio as gr
import torch
import torchaudio
import time
import json
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
import music_tag
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()):
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, progress=progress, max_chunk_size=args.cond_latent_max_chunk_size)
torch.save(conditioning_latents, os.path.join(f'./tortoise/voices/{voice}/', f'cond_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,
'half_p': "Half Precision" in experimentals,
'cond_free': "Conditioning-Free" in experimentals,
}
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': audio,
'text': cut_text,
}
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': audio,
'text': cut_text,
}
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):
audio = audio_cache[f'candidate_{candidate}/result_{line}.wav']['audio']
else:
audio = audio_cache[f'result_{line}.wav']['audio']
audio_clips.append(audio)
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 = {
'text': text,
'delimiter': delimiter,
'emotion': emotion,
'prompt': prompt,
'voice': voice,
'mic_audio': mic_audio,
'preset': preset,
'seed': seed,
'candidates': candidates,
'num_autoregressive_samples': num_autoregressive_samples,
'diffusion_iterations': diffusion_iterations,
'temperature': temperature,
'diffusion_sampler': diffusion_sampler,
'breathing_room': breathing_room,
'experimentals': experimentals,
'time': time.time()-start_time,
}
with open(os.path.join(outdir, f'input.txt'), 'w', encoding="utf-8") as f:
f.write(json.dumps(info, indent='\t') )
print(f"Saved to '{outdir}'")
for path in audio_cache:
info['text'] = audio_cache[path]['text']
metadata = music_tag.load_file(os.path.join(outdir, path))
metadata['lyrics'] = json.dumps(info)
metadata.save()
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"], # + ["K_Euler_A", "DPM++2M"],
value="P",
label="Diffusion Samplers",
type="value",
)
experimentals = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags")
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,
experimentals,
],
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")
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")
args = parser.parse_args()
tts = TextToSpeech(minor_optimizations=not args.low_vram)
main()