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import os |
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import torch |
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import gradio as gr |
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import torchaudio |
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import time |
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from datetime import datetime |
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from tortoise.api import TextToSpeech |
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from tortoise.utils.text import split_and_recombine_text |
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from tortoise.utils.audio import load_audio, load_voice, load_voices |
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VOICE_OPTIONS = [ |
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"angie", |
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"deniro", |
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"freeman", |
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"halle", |
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"lj", |
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"myself", |
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"pat2", |
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"snakes", |
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"tom", |
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"daws", |
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"dreams", |
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"grace", |
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"lescault", |
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"weaver", |
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"applejack", |
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"daniel", |
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"emma", |
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"geralt", |
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"jlaw", |
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"mol", |
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"pat", |
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"rainbow", |
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"tim_reynolds", |
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"atkins", |
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"dortice", |
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"empire", |
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"kennard", |
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"mouse", |
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"william", |
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"jane_eyre", |
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"random", |
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] |
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def inference( |
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text, |
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script, |
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voice, |
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voice_b, |
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seed, |
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split_by_newline, |
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): |
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if text is None or text.strip() == "": |
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with open(script.name) as f: |
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text = f.read() |
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if text.strip() == "": |
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raise gr.Error("Please provide either text or script file with content.") |
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if split_by_newline == "Yes": |
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texts = list(filter(lambda x: x.strip() != "", text.split("\n"))) |
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else: |
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texts = split_and_recombine_text(text) |
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voices = [voice] |
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if voice_b != "disabled": |
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voices.append(voice_b) |
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if len(voices) == 1: |
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voice_samples, conditioning_latents = load_voice(voice) |
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else: |
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voice_samples, conditioning_latents = load_voices(voices) |
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start_time = time.time() |
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all_parts = [] |
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for j, text in enumerate(texts): |
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gen = tts.tts_with_preset( |
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text, |
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voice_samples=voice_samples, |
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conditioning_latents=conditioning_latents, |
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preset="ultra_fast", |
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k=1 |
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) |
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audio_ = gen.squeeze(0).cpu() |
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all_parts.append(audio_) |
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full_audio = torch.cat(all_parts, dim=-1) |
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with open("Tortoise_TTS_Runs_Scripts.log", "a") as f: |
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f.write( |
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f"{datetime.now()} | Voice: {','.join(voices)} | Text: {text} | Time Taken (s): {time.time()-start_time} | Seed: {seed}\n" |
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) |
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output_texts = [f"({j+1}) {texts[j]}" for j in range(len(texts))] |
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return ((24000, full_audio.squeeze().cpu().numpy()), "\n".join(output_texts)) |
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def main(): |
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title = "Tortoise TTS 🐢" |
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description = """ |
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A text-to-speech system which powers lot of organizations in Speech synthesis domain. |
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<br/> |
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a model with strong multi-voice capabilities, highly realistic prosody and intonation. |
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<br/> |
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for faster inference, use the 'ultra_fast' preset and duplicate space if you don't want to wait in a queue. |
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<br/> |
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""" |
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text = gr.Textbox( |
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lines=4, |
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label="Text (Provide either text, or upload a newline separated text file below):", |
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) |
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script = gr.File(label="Upload a text file") |
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voice = gr.Dropdown( |
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VOICE_OPTIONS, value="jane_eyre", label="Select voice:", type="value" |
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) |
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voice_b = gr.Dropdown( |
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VOICE_OPTIONS, |
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value="disabled", |
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label="(Optional) Select second voice:", |
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type="value", |
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) |
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split_by_newline = gr.Radio( |
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["Yes", "No"], |
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label="Split by newline (If [No], it will automatically try to find relevant splits):", |
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type="value", |
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value="No", |
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) |
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output_audio = gr.Audio(label="Combined audio:") |
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output_text = gr.Textbox(label="Split texts with indices:", lines=10) |
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interface = gr.Interface( |
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fn=inference, |
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inputs=[ |
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text, |
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script, |
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voice, |
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voice_b, |
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split_by_newline, |
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], |
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title=title, |
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description=description, |
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outputs=[output_audio, output_text], |
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) |
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interface.launch() |
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if __name__ == "__main__": |
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tts = TextToSpeech(kv_cache=True, use_deepspeed=True, half=True) |
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with open("Tortoise_TTS_Runs_Scripts.log", "a") as f: |
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f.write( |
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f"\n\n-------------------------Tortoise TTS Scripts Logs, {datetime.now()}-------------------------\n" |
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) |
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main() |
