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import os
import gradio as gr
import numpy as np
import torch
import torchaudio
from typing import Tuple, Optional
import soundfile as sf
from s2st_inference import s2st_inference
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
def download_model(tag: str, out_dir: str):
from huggingface_hub import snapshot_download
return snapshot_download(repo_id=tag, local_dir=out_dir)
def s2st(
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
# Download models
os.makedirs(S2UT_DIR, exist_ok=True)
os.makedirs(VOCODER_DIR, exist_ok=True)
s2ut_path = download_model(S2UT_TAG, S2UT_DIR)
vocoder_path = download_model(VOCODER_TAG, VOCODER_DIR)
# Temporary change cwd to model dir so that it loads correctly
cwd = os.getcwd()
os.chdir(s2ut_path)
# Translate wav
out_wav = s2st_inference(
wav,
train_config=os.path.join(
s2ut_path,
'exp',
's2st_train_s2st_discrete_unit_raw_fbank_es_en',
'config.yaml',
),
model_file=os.path.join(
s2ut_path,
'exp',
's2st_train_s2st_discrete_unit_raw_fbank_es_en',
'500epoch.pth',
),
vocoder_file=os.path.join(
vocoder_path,
'checkpoint-450000steps.pkl',
),
vocoder_config=os.path.join(
vocoder_path,
'config.yml',
),
ngpu=NGPU,
)
# 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():
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=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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