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import gradio as gr
import numpy as np
from librosa import resample
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base.en", chunk_length_s=30)
def transcribe(audio_in):
orig_sr, samples = audio_in
min_s, max_s = min(samples), max(samples)
range_in = (max_s - min_s)
samples_scl = np.array(samples) / range_in
min_scl = min_s / range_in
samples_f = 2.0 * (samples_scl - min_scl) - 1.0
resamples = resample(samples_f, orig_sr=orig_sr, target_sr=16000)
prediction = pipe(resamples.copy(), batch_size=8)
return prediction["text"].strip().lower()
with gr.Blocks() as demo:
gr.Markdown("""
# 9103H 2024F Audio Transcription.
## API for [whisper-base.en](https://huggingface.co/openai/whisper-base.en) english model\
to help check [HW03](https://github.com/DM-GY-9103-2024F-H/HW03) exercises.
""")
gr.Interface(
transcribe,
inputs=gr.Audio(type="numpy"),
outputs="text",
cache_examples=True,
examples=[["./audio/plain_01.wav"]]
)
if __name__ == "__main__":
demo.launch()
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