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| import os, sys | |
| import tempfile | |
| import gradio as gr | |
| import numpy as np | |
| from typing import Tuple, List | |
| # Setup and installation | |
| os.system("git clone https://github.com/neonbjb/tortoise-tts.git") | |
| os.system("cd tortoise-tts") | |
| os.system("git reset --hard 8c0b3855bfb5312adf2b000b52cf5cfa2830c310") | |
| sys.path.append("./tortoise-tts/") | |
| os.system("pip install -r ./tortoise-tts/requirements.txt") | |
| os.system("python ./tortoise-tts/setup.py install") | |
| import torch | |
| import torchaudio | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from tortoise.api import TextToSpeech | |
| from tortoise.utils.audio import load_audio, load_voice | |
| # Download and instantiate model | |
| tts = TextToSpeech() | |
| # Display parameters | |
| VOICES = ["random","train_atkins","train_daws","train_dotrice","train_dreams","train_empire","train_grace","train_kennard","train_lescault","train_mouse","angie","applejack","daniel","deniro","emma","freeman","geralt","halle","jlaw","lj","mol","myself","pat","pat2","rainbow","snakes","tim_reynolds","tom","weaver","william"] | |
| DEFAULT_VOICE = "random" | |
| PRESETS = ["ultra_fast", "fast", "standard", "high_quality"] | |
| DEFAULT_PRESET = "fast" | |
| DEFAULT_TEXT = "Hello, world!" | |
| README = """# TorToiSe | |
| forked from https://huggingface.co/spaces/mdnestor/tortoise | |
| Tortoise is a text-to-speech model developed by James Betker. It is capable of zero-shot voice cloning from a small set of voice samples. GitHub repo: [neonbjb/tortoise-tts](https://github.com/neonbjb/tortoise-tts). | |
| ## Usage | |
| 1. Select a model preset and type the text to speak. | |
| 2. Load a voice - either by choosing a preset, uploading audio files, or recording via microphone. Select the option to split audio into chunks if the clips are much longer than 10 seconds each. Follow the guidelines in the [voice customization guide](https://github.com/neonbjb/tortoise-tts#voice-customization-guide). | |
| 3. Click **Generate**, and wait - it's called *tortoise* for a reason! | |
| """ | |
| TORTOISE_SR_IN = 22050 | |
| TORTOISE_SR_OUT = 24000 | |
| def chunk_audio(t: torch.Tensor, sample_rate: int, chunk_duration_sec: int) -> List[torch.Tensor]: | |
| duration = t.shape[1] / sample_rate | |
| num_chunks = 1 + int(duration/chunk_duration_sec) | |
| chunks = [t[:,(sample_rate*chunk_duration_sec*i):(sample_rate*chunk_duration_sec*(i+1))] for i in range(num_chunks)] | |
| # remove 0-width chunks | |
| chunks = [chunk for chunk in chunks if chunk.shape[1]>0] | |
| return chunks | |
| def tts_main(voice_samples: List[torch.Tensor], text: str, model_preset: str) -> str: | |
| gen = tts.tts_with_preset( | |
| text, | |
| voice_samples=voice_samples, | |
| conditioning_latents=None, | |
| preset=model_preset | |
| ) | |
| torchaudio.save("generated.wav", gen.squeeze(0).cpu(), TORTOISE_SR_OUT) | |
| return "generated.wav" | |
| def tts_from_preset(voice: str, text, model_preset): | |
| voice_samples, _ = load_voice(voice) | |
| return tts_main(voice_samples, text, model_preset) | |
| def tts_from_files(files: List[tempfile._TemporaryFileWrapper], do_chunk, text, model_preset): | |
| voice_samples = [load_audio(f.name, TORTOISE_SR_IN) for f in files] | |
| if do_chunk: | |
| voice_samples = [chunk for t in voice_samples for chunk in chunk_audio(t, TORTOISE_SR_IN, 10)] | |
| return tts_main(voice_samples, text, model_preset) | |
| def tts_from_recording(recording: Tuple[int, np.ndarray], do_chunk, text, model_preset): | |
| sample_rate, audio = recording | |
| # normalize- https://github.com/neonbjb/tortoise-tts/blob/main/tortoise/utils/audio.py#L16 | |
| norm_fix = 1 | |
| if audio.dtype == np.int32: | |
| norm_fix = 2**31 | |
| elif audio.dtype == np.int16: | |
| norm_fix = 2**15 | |
| audio = torch.FloatTensor(audio.T) / norm_fix | |
| if len(audio.shape) > 1: | |
| # convert to mono | |
| audio = torch.mean(audio, axis=0).unsqueeze(0) | |
| audio = torchaudio.transforms.Resample(sample_rate, TORTOISE_SR_IN)(audio) | |
| if do_chunk: | |
| voice_samples = chunk_audio(audio, TORTOISE_SR_IN, 10) | |
| else: | |
| voice_samples = [audio] | |
| return tts_main(voice_samples, text, model_preset) | |
| def tts_from_url(audio_url, start_time, end_time, do_chunk, text, model_preset): | |
| os.system(f"yt-dlp -x --audio-format mp3 --force-overwrites {audio_url} -o audio.mp3") | |
| audio = load_audio("audio.mp3", TORTOISE_SR_IN) | |
| audio = audio[:,start_time*TORTOISE_SR_IN:end_time*TORTOISE_SR_IN] | |
| if do_chunk: | |
| voice_samples = chunk_audio(audio, TORTOISE_SR_IN, 10) | |
| else: | |
| voice_samples = [audio] | |
| return tts_main(voice_samples, text, model_preset) | |
| with gr.Blocks() as demo: | |
| gr.Markdown(README) | |
| preset = gr.Dropdown(PRESETS, label="Model preset", value=DEFAULT_PRESET) | |
| text = gr.Textbox(label="Text to speak", value=DEFAULT_TEXT) | |
| do_chunk_label = "Split audio into chunks? (for audio much longer than 10 seconds.)" | |
| do_chunk_default = True | |
| with gr.Tab("Choose preset voice"): | |
| inp1 = gr.Dropdown(VOICES, value=DEFAULT_VOICE, label="Preset voice") | |
| btn1 = gr.Button("Generate") | |
| with gr.Tab("Upload audio"): | |
| inp2 = gr.File(file_count="multiple") | |
| do_chunk2 = gr.Checkbox(label=do_chunk_label, value=do_chunk_default) | |
| btn2 = gr.Button("Generate") | |
| with gr.Tab("Record audio"): | |
| inp3 = gr.Audio(source="microphone") | |
| do_chunk3 = gr.Checkbox(label=do_chunk_label, value=do_chunk_default) | |
| btn3 = gr.Button("Generate") | |
| # with gr.Tab("From YouTube"): | |
| # inp4 = gr.Textbox(label="URL") | |
| # do_chunk4 = gr.Checkbox(label=do_chunk_label, value=do_chunk_default) | |
| # start_time = gr.Number(label="Start time (seconds)", precision=0) | |
| # end_time = gr.Number(label="End time (seconds)", precision=0) | |
| # btn4 = gr.Button("Generate") | |
| audio_out = gr.Audio() | |
| btn1.click( | |
| tts_from_preset, | |
| [inp1, text, preset], | |
| [audio_out], | |
| ) | |
| btn2.click( | |
| tts_from_files, | |
| [inp2, do_chunk2, text, preset], | |
| [audio_out], | |
| ) | |
| btn3.click( | |
| tts_from_recording, | |
| [inp3, do_chunk3, text, preset], | |
| [audio_out], | |
| ) | |
| # btn4.click( | |
| # tts_from_url, | |
| # [inp4, start_time, end_time, do_chunk4, text, preset], | |
| # [audio_out], | |
| # ) | |
| demo.launch() |