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