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Matcha-TTS

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Matcha-TTS / app.py

Shivam Mehta

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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_ljspeech",
	vocoder="hifigan_T2_v1",
	spk=0,
	)


	MATCHA_TTS_LOC = lambda x: LOCATION / f"{x}.ckpt" # noqa: E731
	VOCODER_LOC = lambda x: LOCATION / f"{x}" # noqa: E731
	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"])

	# get device
	device = get_device(args)

	# Load default models
	matcha_ljspeech = load_matcha(args.model, MATCHA_TTS_LOC(args.model), device)
	hifigan_T2_v1, hifigan_T2_v1_denoiser = load_vocoder(args.vocoder, VOCODER_LOC(args.vocoder), device)

	matcha_vctk = load_matcha("matcha_vctk", MATCHA_TTS_LOC("matcha_vctk"), device)
	hifigan_univ_v1, hifigan_univ_v1_denoiser = load_vocoder("hifigan_univ_v1", VOCODER_LOC("hifigan_univ_v1"), device)



	def load_model_ui(model_type, textbox):
	model_name = RADIO_OPTIONS[model_type]["model"]

	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

	if spk is None:
	output = matcha_ljspeech.synthesise(
	text,
	text_length,
	n_timesteps=n_timesteps,
	temperature=temperature,
	spks=None,
	length_scale=length_scale,
	)
	output["waveform"] = to_waveform(output["mel"], hifigan_T2_v1, hifigan_T2_v1_denoiser)
	else:
	output = matcha_vctk.synthesise(
	text,
	text_length,
	n_timesteps=n_timesteps,
	temperature=temperature,
	spks=spk,
	length_scale=length_scale,
	)
	output["waveform"] = to_waveform(output["mel"], hifigan_univ_v1, hifigan_univ_v1_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):
	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):
	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


	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 audio examples below and 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)
	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=0.85,
	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(concurrency_count=5).launch()