import tempfile from argparse import Namespace from pathlib import Path import gradio as gr import soundfile as sf import torch from matcha.cli import (MATCHA_URLS, VOCODER_URL, assert_model_downloaded, get_device, load_matcha, load_vocoder, process_text, to_waveform) from matcha.utils.utils import get_user_data_dir, plot_tensor LOCATION = Path(get_user_data_dir()) args = Namespace( cpu=False, model="matcha_ljspeech", vocoder="hifigan_T2_v1", spk=None, ) MATCHA_TTS_LOC = LOCATION / f"{args.model}.ckpt" VOCODER_LOC = LOCATION / f"{args.vocoder}" LOGO_URL = "https://shivammehta25.github.io/Matcha-TTS/images/logo.png" assert_model_downloaded(MATCHA_TTS_LOC, MATCHA_URLS[args.model]) assert_model_downloaded(VOCODER_LOC, VOCODER_URL[args.vocoder]) device = get_device(args) model = load_matcha(args.model, MATCHA_TTS_LOC, device) vocoder, denoiser = load_vocoder(args.vocoder, VOCODER_LOC, device) @torch.inference_mode() def process_text_gradio(text): output = process_text(1, text, device) return output["x_phones"][1::2], output["x"], output["x_lengths"] @torch.inference_mode() def synthesise_mel(text, text_length, n_timesteps, temperature, length_scale): output = model.synthesise( text, text_length, n_timesteps=n_timesteps, temperature=temperature, spks=args.spk, length_scale=length_scale, ) output["waveform"] = to_waveform(output["mel"], vocoder, denoiser) with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: sf.write(fp.name, output["waveform"], 22050, "PCM_24") return fp.name, plot_tensor(output["mel"].squeeze().cpu().numpy()) def run_full_synthesis(text, n_timesteps, mel_temp, length_scale): phones, text, text_lengths = process_text_gradio(text) audio, mel_spectrogram = synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale) return phones, audio, mel_spectrogram def main(): description = """# 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching ### [Shivam Mehta](https://www.kth.se/profile/smehta), [Ruibo Tu](https://www.kth.se/profile/ruibo), [Jonas Beskow](https://www.kth.se/profile/beskow), [Éva Székely](https://www.kth.se/profile/szekely), and [Gustav Eje Henter](https://people.kth.se/~ghe/) We propose 🍵 Matcha-TTS, a new approach to non-autoregressive neural TTS, that uses conditional flow matching (similar to rectified flows) to speed up ODE-based speech synthesis. Our method: * Is probabilistic * Has compact memory footprint * Sounds highly natural * Is very fast to synthesise from Check out our [demo page](https://shivammehta25.github.io/Matcha-TTS). Read our [arXiv preprint for more details](https://arxiv.org/abs/2309.03199). Code is available in our [GitHub repository](https://github.com/shivammehta25/Matcha-TTS), along with pre-trained models. Cached examples are available at the bottom of the page. Note: Synthesis speed may be slower than in our paper due to I/O latency and because this instance runs on CPUs. """ with gr.Blocks(title="🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching") as demo: processed_text = gr.State(value=None) processed_text_len = gr.State(value=None) with gr.Box(): with gr.Row(): gr.Markdown(description, scale=3) gr.Image(LOGO_URL, label="Matcha-TTS logo", height=150, width=150, scale=1, show_label=False) with gr.Box(): with gr.Row(): gr.Markdown("# Text Input") with gr.Row(): text = gr.Textbox(value="", lines=2, label="Text to synthesise") with gr.Row(): gr.Markdown("### Hyper parameters") with gr.Row(): n_timesteps = gr.Slider( label="Number of ODE steps", minimum=1, maximum=100, step=1, value=10, interactive=True, ) length_scale = gr.Slider( label="Length scale (Speaking rate)", minimum=0.5, maximum=1.5, step=0.05, value=1.0, interactive=True, ) mel_temp = gr.Slider( label="Sampling temperature", minimum=0.00, maximum=2.001, step=0.16675, value=0.667, interactive=True, ) synth_btn = gr.Button("Synthesise") with gr.Box(): with gr.Row(): gr.Markdown("### Phonetised text") phonetised_text = gr.Textbox(interactive=False, scale=10, label="Phonetised text") with gr.Box(): with gr.Row(): mel_spectrogram = gr.Image(interactive=False, label="mel spectrogram") # with gr.Row(): audio = gr.Audio(interactive=False, label="Audio") with gr.Row(): examples = gr.Examples( # pylint: disable=unused-variable examples=[ [ "We propose Matcha-TTS, a new approach to non-autoregressive neural TTS, that uses conditional flow matching (similar to rectified flows) to speed up O D E-based speech synthesis.", 50, 0.677, 1.0, ], [ "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.", 2, 0.677, 1.0, ], [ "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.", 4, 0.677, 1.0, ], [ "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.", 10, 0.677, 1.0, ], [ "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.", 50, 0.677, 1.0, ], [ "The narrative of these events is based largely on the recollections of the participants.", 10, 0.677, 1.0, ], [ "The jury did not believe him, and the verdict was for the defendants.", 10, 0.677, 1.0, ], ], fn=run_full_synthesis, inputs=[text, n_timesteps, mel_temp, length_scale], outputs=[phonetised_text, audio, mel_spectrogram], cache_examples=True, ) synth_btn.click( fn=process_text_gradio, inputs=[ text, ], outputs=[phonetised_text, processed_text, processed_text_len], api_name="matcha_tts", queue=True, ).then( fn=synthesise_mel, inputs=[processed_text, processed_text_len, n_timesteps, mel_temp, length_scale], outputs=[audio, mel_spectrogram], ) demo.queue(concurrency_count=5).launch() if __name__ == "__main__": main()