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55bf388 4defacc ed95978 55bf388 4defacc a202342 f5a907f a202342 4defacc ed95978 4defacc ed95978 4defacc ed95978 55bf388 ed95978 55bf388 ed95978 55bf388 ed95978 55bf388 | 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 | import os
import gradio as gr
import torchaudio
from typing import Tuple, Optional
import soundfile as sf
from s2st_inference import s2st_inference
from utils import download_model
SAMPLE_RATE = 16000
MAX_INPUT_LENGTH = 60 # seconds
S2UT_TAG = 'espnet/jiyang_tang_cvss-c_es-en_discrete_unit'
S2UT_DIR = 'model'
VOCODER_TAG = 'espnet/cvss-c_en_wavegan_hubert_vocoder'
VOCODER_DIR = 'vocoder'
NGPU = 0
BEAM_SIZE = 1
class App:
def __init__(self):
# Download models
os.makedirs(S2UT_DIR, exist_ok=True)
os.makedirs(VOCODER_DIR, exist_ok=True)
self.s2ut_path = download_model(S2UT_TAG, S2UT_DIR)
self.vocoder_path = download_model(VOCODER_TAG, VOCODER_DIR)
def s2st(
self,
audio_source: str,
input_audio_mic: Optional[str],
input_audio_file: Optional[str],
):
if audio_source == 'file':
input_path = input_audio_file
else:
input_path = input_audio_mic
if input_path is None:
gr.Error(f"Input audio is too long. Truncated to {MAX_INPUT_LENGTH} seconds.")
return (None, None), None
orig_wav, orig_sr = torchaudio.load(input_path)
wav = torchaudio.functional.resample(orig_wav, orig_freq=orig_sr, new_freq=SAMPLE_RATE)
max_length = int(MAX_INPUT_LENGTH * SAMPLE_RATE)
if wav.shape[1] > max_length:
wav = wav[:, :max_length]
gr.Warning(f"Input audio is too long. Truncated to {MAX_INPUT_LENGTH} seconds.")
wav = wav[0] # mono
# Temporary change cwd to model dir so that it loads correctly
cwd = os.getcwd()
os.chdir(self.s2ut_path)
# Translate wav
out_wav = s2st_inference(
wav,
train_config=os.path.join(
self.s2ut_path,
'exp',
's2st_train_s2st_discrete_unit_raw_fbank_es_en',
'config.yaml',
),
model_file=os.path.join(
self.s2ut_path,
'exp',
's2st_train_s2st_discrete_unit_raw_fbank_es_en',
'500epoch.pth',
),
vocoder_file=os.path.join(
self.vocoder_path,
'checkpoint-450000steps.pkl',
),
vocoder_config=os.path.join(
self.vocoder_path,
'config.yml',
),
ngpu=NGPU,
beam_size=BEAM_SIZE,
)
# Restore working directory
os.chdir(cwd)
# Save result
output_path = 'output.wav'
sf.write(
output_path,
out_wav,
16000,
"PCM_16",
)
return output_path, f'Source: {audio_source}'
def update_audio_ui(audio_source: str) -> Tuple[dict, dict]:
mic = audio_source == "microphone"
return (
gr.update(visible=mic, value=None), # input_audio_mic
gr.update(visible=not mic, value=None), # input_audio_file
)
def main():
app = App()
with gr.Blocks() as demo:
with gr.Group():
with gr.Row() as audio_box:
audio_source = gr.Radio(
label="Audio source",
choices=["file", "microphone"],
value="file",
)
input_audio_mic = gr.Audio(
label="Input speech",
type="filepath",
source="microphone",
visible=False,
)
input_audio_file = gr.Audio(
label="Input speech",
type="filepath",
source="upload",
visible=True,
)
btn = gr.Button("Translate")
with gr.Column():
output_audio = gr.Audio(
label="Translated speech",
autoplay=False,
streaming=False,
type="numpy",
)
output_text = gr.Textbox(label="Translated text")
audio_source.change(
fn=update_audio_ui,
inputs=audio_source,
outputs=[
input_audio_mic,
input_audio_file,
],
queue=False,
api_name=False,
)
btn.click(
fn=app.s2st,
inputs=[
audio_source,
input_audio_mic,
input_audio_file,
],
outputs=[output_audio, output_text],
api_name="run",
)
demo.queue(max_size=50).launch()
if __name__ == '__main__':
main()
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