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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_URLS,
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_vctk",
vocoder="hifigan_univ_v1",
spk=0,
)
CURRENTLY_LOADED_MODEL = args.model
def MATCHA_TTS_LOC(x):
return LOCATION / f"{x}.ckpt"
def VOCODER_LOC(x):
return LOCATION / f"{x}"
LOGO_URL = "https://shivammehta25.github.io/Matcha-TTS/images/logo.png"
RADIO_OPTIONS = {
"Multi Speaker (VCTK)": {
"model": "matcha_vctk",
"vocoder": "hifigan_univ_v1",
},
"Single Speaker (LJ Speech)": {
"model": "matcha_ljspeech",
"vocoder": "hifigan_T2_v1",
},
}
# Ensure all the required models are downloaded
assert_model_downloaded(MATCHA_TTS_LOC("matcha_ljspeech"), MATCHA_URLS["matcha_ljspeech"])
assert_model_downloaded(VOCODER_LOC("hifigan_T2_v1"), VOCODER_URLS["hifigan_T2_v1"])
assert_model_downloaded(MATCHA_TTS_LOC("matcha_vctk"), MATCHA_URLS["matcha_vctk"])
assert_model_downloaded(VOCODER_LOC("hifigan_univ_v1"), VOCODER_URLS["hifigan_univ_v1"])
device = get_device(args)
# Load default model
model = load_matcha(args.model, MATCHA_TTS_LOC(args.model), device)
vocoder, denoiser = load_vocoder(args.vocoder, VOCODER_LOC(args.vocoder), device)
def load_model(model_name, vocoder_name):
model = load_matcha(model_name, MATCHA_TTS_LOC(model_name), device)
vocoder, denoiser = load_vocoder(vocoder_name, VOCODER_LOC(vocoder_name), device)
return model, vocoder, denoiser
def load_model_ui(model_type, textbox):
model_name, vocoder_name = RADIO_OPTIONS[model_type]["model"], RADIO_OPTIONS[model_type]["vocoder"]
global model, vocoder, denoiser, CURRENTLY_LOADED_MODEL # pylint: disable=global-statement
if CURRENTLY_LOADED_MODEL != model_name:
model, vocoder, denoiser = load_model(model_name, vocoder_name)
CURRENTLY_LOADED_MODEL = model_name
if model_name == "matcha_ljspeech":
spk_slider = gr.update(visible=False, value=-1)
single_speaker_examples = gr.update(visible=True)
multi_speaker_examples = gr.update(visible=False)
length_scale = gr.update(value=0.95)
else:
spk_slider = gr.update(visible=True, value=0)
single_speaker_examples = gr.update(visible=False)
multi_speaker_examples = gr.update(visible=True)
length_scale = gr.update(value=0.85)
return (
textbox,
gr.update(interactive=True),
spk_slider,
single_speaker_examples,
multi_speaker_examples,
length_scale,
)
@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, spk):
spk = torch.tensor([spk], device=device, dtype=torch.long) if spk >= 0 else None
output = model.synthesise(
text,
text_length,
n_timesteps=n_timesteps,
temperature=temperature,
spks=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 multispeaker_example_cacher(text, n_timesteps, mel_temp, length_scale, spk):
global CURRENTLY_LOADED_MODEL # pylint: disable=global-statement
if CURRENTLY_LOADED_MODEL != "matcha_vctk":
global model, vocoder, denoiser # pylint: disable=global-statement
model, vocoder, denoiser = load_model("matcha_vctk", "hifigan_univ_v1")
CURRENTLY_LOADED_MODEL = "matcha_vctk"
phones, text, text_lengths = process_text_gradio(text)
audio, mel_spectrogram = synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk)
return phones, audio, mel_spectrogram
def ljspeech_example_cacher(text, n_timesteps, mel_temp, length_scale, spk=-1):
global CURRENTLY_LOADED_MODEL # pylint: disable=global-statement
if CURRENTLY_LOADED_MODEL != "matcha_ljspeech":
global model, vocoder, denoiser # pylint: disable=global-statement
model, vocoder, denoiser = load_model("matcha_ljspeech", "hifigan_T2_v1")
CURRENTLY_LOADED_MODEL = "matcha_ljspeech"
phones, text, text_lengths = process_text_gradio(text)
audio, mel_spectrogram = synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk)
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.
"""
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)
with gr.Column():
gr.Image(LOGO_URL, label="Matcha-TTS logo", height=50, width=50, scale=1, show_label=False)
html = '<br><iframe width="560" height="315" src="https://www.youtube.com/embed/xmvJkz3bqw0?si=jN7ILyDsbPwJCGoa" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>'
gr.HTML(html)
with gr.Box():
radio_options = list(RADIO_OPTIONS.keys())
model_type = gr.Radio(
radio_options, value=radio_options[0], label="Choose a Model", interactive=True, container=False
)
with gr.Row():
gr.Markdown("# Text Input")
with gr.Row():
text = gr.Textbox(value="", lines=2, label="Text to synthesise", scale=3)
spk_slider = gr.Slider(
minimum=0, maximum=107, step=1, value=args.spk, label="Speaker ID", interactive=True, scale=1
)
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(visible=False) as example_row_lj_speech:
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,
0.95,
],
[
"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,
0.95,
],
[
"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,
0.95,
],
[
"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,
0.95,
],
[
"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,
0.95,
],
[
"The narrative of these events is based largely on the recollections of the participants.",
10,
0.677,
0.95,
],
[
"The jury did not believe him, and the verdict was for the defendants.",
10,
0.677,
0.95,
],
],
fn=ljspeech_example_cacher,
inputs=[text, n_timesteps, mel_temp, length_scale],
outputs=[phonetised_text, audio, mel_spectrogram],
cache_examples=True,
)
with gr.Row() as example_row_multispeaker:
multi_speaker_examples = gr.Examples( # pylint: disable=unused-variable
examples=[
[
"Hello everyone! I am speaker 0 and I am here to tell you that Matcha-TTS is amazing!",
10,
0.677,
0.85,
0,
],
[
"Hello everyone! I am speaker 16 and I am here to tell you that Matcha-TTS is amazing!",
10,
0.677,
0.85,
16,
],
[
"Hello everyone! I am speaker 44 and I am here to tell you that Matcha-TTS is amazing!",
50,
0.677,
0.85,
44,
],
[
"Hello everyone! I am speaker 45 and I am here to tell you that Matcha-TTS is amazing!",
50,
0.677,
0.85,
45,
],
[
"Hello everyone! I am speaker 58 and I am here to tell you that Matcha-TTS is amazing!",
4,
0.677,
0.85,
58,
],
],
fn=multispeaker_example_cacher,
inputs=[text, n_timesteps, mel_temp, length_scale, spk_slider],
outputs=[phonetised_text, audio, mel_spectrogram],
cache_examples=True,
label="Multi Speaker Examples",
)
model_type.change(lambda x: gr.update(interactive=False), inputs=[synth_btn], outputs=[synth_btn]).then(
load_model_ui,
inputs=[model_type, text],
outputs=[text, synth_btn, spk_slider, example_row_lj_speech, example_row_multispeaker, length_scale],
)
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, spk_slider],
outputs=[audio, mel_spectrogram],
)
demo.queue().launch(share=True)
if __name__ == "__main__":
main()
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