app.py

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
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()