File size: 5,550 Bytes
92df4f5 35cd28c 92df4f5 bbb1375 d3127d4 92df4f5 d3127d4 92df4f5 bbb1375 92df4f5 35cd28c 92df4f5 bbb1375 92df4f5 bbb1375 92df4f5 bbb1375 92df4f5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 |
import numpy as np
import onnxruntime
import utils
from text import text_to_sequence, sequence_to_text
import torch
import gradio as gr
import soundfile as sf
import tempfile
import yaml
def intersperse(lst, item):
result = [item] * (len(lst) * 2 + 1)
result[1::2] = lst
return result
def process_text(i: int, text: str, device: torch.device):
print(f"[{i}] - Input text: {text}")
x = torch.tensor(
intersperse(text_to_sequence(text, ["catalan_cleaners"]), 0),
dtype=torch.long,
device=device,
)[None]
x_lengths = torch.tensor([x.shape[-1]], dtype=torch.long, device=device)
x_phones = sequence_to_text(x.squeeze(0).tolist())
print(x_phones)
return x.numpy(), x_lengths.numpy()
MODEL_PATH_MATCHA_MEL="matcha_multispeaker_cat_opset_15.onnx"
MODEL_PATH_MATCHA="matcha_hifigan_multispeaker_cat.onnx"
MODEL_PATH_VOCOS="mel_spec_22khz.onnx"
CONFIG_PATH="config_22khz.yaml"
sess_options = onnxruntime.SessionOptions()
model_matcha_mel= onnxruntime.InferenceSession(str(MODEL_PATH_MATCHA_MEL), sess_options=sess_options, providers=["CPUExecutionProvider"])
model_vocos = onnxruntime.InferenceSession(str(MODEL_PATH_VOCOS), sess_options=sess_options, providers=["CPUExecutionProvider"])
model_matcha = onnxruntime.InferenceSession(str(MODEL_PATH_MATCHA), sess_options=sess_options, providers=["CPUExecutionProvider"])
def vocos_inference(mel):
with open(CONFIG_PATH, "r") as f:
config = yaml.safe_load(f)
params = config["feature_extractor"]["init_args"]
sample_rate = params["sample_rate"]
n_fft= params["n_fft"]
hop_length= params["hop_length"]
win_length = n_fft
# ONNX inference
mag, x, y = model_vocos.run(
None,
{
"mels": mel
},
)
# complex spectrogram from vocos output
spectrogram = mag * (x + 1j * y)
window = torch.hann_window(win_length)
# Inverse stft
pad = (win_length - hop_length) // 2
spectrogram = torch.tensor(spectrogram)
B, N, T = spectrogram.shape
print("Spectrogram synthesized shape", spectrogram.shape)
# Inverse FFT
ifft = torch.fft.irfft(spectrogram, n_fft, dim=1, norm="backward")
ifft = ifft * window[None, :, None]
# Overlap and Add
output_size = (T - 1) * hop_length + win_length
y = torch.nn.functional.fold(
ifft, output_size=(1, output_size), kernel_size=(1, win_length), stride=(1, hop_length),
)[:, 0, 0, pad:-pad]
# Window envelope
window_sq = window.square().expand(1, T, -1).transpose(1, 2)
window_envelope = torch.nn.functional.fold(
window_sq, output_size=(1, output_size), kernel_size=(1, win_length), stride=(1, hop_length),
).squeeze()[pad:-pad]
# Normalize
assert (window_envelope > 1e-11).all()
y = y / window_envelope
return y
def tts(text:str, spk_id:int):
sid = np.array([int(spk_id)]) if spk_id is not None else None
text_matcha , text_lengths = process_text(0,text,"cpu")
# MATCHA VOCOS
inputs = {
"x": text_matcha,
"x_lengths": text_lengths,
"scales": np.array([0.667, 1.0], dtype=np.float32),
"spks": sid
}
mel, mel_lengths = model_matcha_mel.run(None, inputs)
# vocos inference
wavs_vocos = vocos_inference(mel)
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp_matcha_vocos:
sf.write(fp_matcha_vocos.name, wavs_vocos.squeeze(0), 22050, "PCM_24")
#MATCHA HIFIGAN
inputs = {
"x": text_matcha,
"x_lengths": text_lengths,
"scales": np.array([0.667, 1.0], dtype=np.float32),
"spks": sid
}
wavs, wav_lengths = model_matcha.run(None, inputs)
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp_matcha:
sf.write(fp_matcha.name, wavs.squeeze(0), 22050, "PCM_24")
return fp_matcha_vocos.name, fp_matcha.name
## GUI space
title = """
<div style="text-align: center; max-width: 700px; margin: 0 auto;">
<div
style="display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem;"
> <h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;">
TTS Vocoder Comparison
</h1> </div>
</div>
"""
description = """
🍵 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
For vocoders we use Hifigan universal version and Vocos trained in a catalan set of ~28 hours.
Matcha was trained using openslr69 and festcat datasets
"""
article = "Training and demo by BSC."
vits2_inference = gr.Interface(
fn=tts,
inputs=[
gr.Textbox(
value="m'ha costat desenvolupar molt una veu, i ara que la tinc no estaré en silenci.",
max_lines=1,
label="Input text",
),
gr.Slider(
1,
47,
value=10,
step=1,
label="Speaker id",
info=f"Models are trained on 47 speakers. You can prompt the model using one of these speaker ids.",
),
],
outputs=[gr.Audio(label="Matcha vocos", interactive=False, type="filepath"),
gr.Audio(label="Matcha", interactive=False, type="filepath")]
)
demo = gr.Blocks()
with demo:
gr.Markdown(title)
gr.Markdown(description)
gr.TabbedInterface([vits2_inference], ["Multispeaker"])
gr.Markdown(article)
demo.queue(max_size=10)
demo.launch(show_api=False, server_name="0.0.0.0", server_port=7860)
|