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import gradio as gr
from huggingface_hub.utils import get_token
import requests
import base64
from model import model_id, transcribe_audio_local
token = get_token()
def read_file_as_base64(file_path: str) -> str:
with open(file_path, "rb") as f:
return base64.b64encode(f.read()).decode()
def transcribe_audio(audio: str) -> str:
print(f"{audio=}")
if audio is None:
raise gr.Error(
"Please wait a moment for the audio to be uploaded, then click the button again."
)
# resample to 16k mono to reduce file size
import subprocess
import os
audio_resampled = audio.replace(".mp3", "_resampled.mp3")
subprocess.run(
[
"ffmpeg",
"-i",
audio,
"-ac",
"1",
"-ar",
"16000",
audio_resampled,
"-y",
],
check=True,
)
b64 = read_file_as_base64(audio_resampled)
url = f"https://api-inference.huggingface.co/models/{model_id}"
headers = {
"Authorization": f"Bearer {token}",
"Content-Type": "application/json",
"x-wait-for-model": "true",
}
data = {
"inputs": b64,
"parameters": {
"generate_kwargs": {
"return_timestamps": True,
}
},
}
response = requests.post(url, headers=headers, json=data)
print(f"{response.text=}")
out = response.json()
print(f"{out=}")
return out["text"]
with gr.Blocks() as demo:
gr.Markdown("# TWASR: Chinese (Taiwan) Automatic Speech Recognition.")
gr.Markdown("Upload an audio file or record your voice to transcribe it to text.")
gr.Markdown(
"First load may take a while to initialize the model, following requests will be faster."
)
with gr.Row():
audio_input = gr.Audio(
label="Audio", type="filepath", show_download_button=True
)
text_output = gr.Textbox(label="Transcription")
transcribe_local_button = gr.Button(
"Transcribe with Transformers", variant="primary"
)
transcribe_button = gr.Button("Transcribe with Inference API", variant="secondary")
transcribe_local_button.click(
fn=transcribe_audio_local, inputs=[audio_input], outputs=[text_output]
)
transcribe_button.click(
fn=transcribe_audio, inputs=[audio_input], outputs=[text_output]
)
gr.Examples(
[
["./examples/audio1.mp3"],
["./examples/audio2.mp3"],
],
inputs=[audio_input],
outputs=[text_output],
fn=transcribe_audio_local,
cache_examples=True,
cache_mode="lazy",
run_on_click=True,
)
gr.Markdown(
f"Current model: {model_id}. For more information, visit the [model hub](https://huggingface.co./{model_id})."
)
if __name__ == "__main__":
demo.launch()
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