File size: 1,442 Bytes
e88c0b0 7f85a80 e88c0b0 | 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 | import os
import gradio as gr
import spaces
from transformers import pipeline
MODEL_ID = "NCAIR1/NigerianAccentedEnglish"
HF_TOKEN = os.environ.get("HF_TOKEN") or None
API_SECRET = os.environ.get("API_SECRET", "")
# Loaded once on CPU at startup. Only moved to the GPU inside the
# @spaces.GPU-decorated function, for the duration of that call — that's how
# ZeroGPU's shared/ephemeral GPU allocation model works.
pipe = pipeline("automatic-speech-recognition", model=MODEL_ID, token=HF_TOKEN)
@spaces.GPU(duration=45)
def transcribe(audio_path, secret):
if API_SECRET and secret != API_SECRET:
raise gr.Error("Unauthorized")
if audio_path is None:
raise gr.Error("No audio data received.")
pipe.model.to("cuda")
try:
result = pipe(
audio_path,
generate_kwargs={
"language": "en",
"task": "transcribe",
"num_beams": 5,
"no_repeat_ngram_size": 3,
},
)
finally:
pipe.model.to("cpu")
text = result.get("text", "") if isinstance(result, dict) else ""
return text.strip()
demo = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(type="filepath", label="Audio chunk"),
gr.Textbox(label="Secret", type="password"),
],
outputs=gr.Textbox(label="Transcript"),
api_name="transcribe",
)
if __name__ == "__main__":
demo.queue().launch()
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