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import os
os.system("pip install git+https://github.com/openai/whisper.git")
import whisper
from flask import Flask, jsonify, request
import requests
import time
from transformers import pipeline
from datasets import load_dataset
model = whisper.load_model("small")
pipe = pipeline(
"automatic-speech-recognition",
model="openai/whisper-small.en",
chunk_length_s=15,
device=model.device,
)
app = Flask(__name__)
app.config['TIMEOUT'] = 60 * 10 # 10 mins
@app.route("/")
def indexApi():
return jsonify({"output": "okay"})
@app.route("/run", methods=['POST'])
def runApi():
start_time = time.time()
audio_url = request.form.get("audio_url")
response = requests.get(audio_url)
if response.status_code == requests.codes.ok:
with open("audio.mp3", "wb") as f:
f.write(response.content)
else:
return jsonify({
"result": "Unable to save file, status code: {response.status_code}" ,
}), 400
audio = "audio.mp3"
audioOri = whisper.load_audio(audio)
# audio = whisper.pad_or_trim(audioOri)
# mel = whisper.log_mel_spectrogram(audio).to(model.device)
# _, probs = model.detect_language(mel)
# options = whisper.DecodingOptions(fp16 = False)
# result = whisper.decode(model, mel, options)
# test 2
# ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# sample = ds[0]["audio"]
prediction = pipe(audioOri)["text"]
end_time = time.time()
total_time = end_time - start_time
return jsonify({
"audio_url": audio_url,
"result": prediction,
"exec_time_sec": total_time
})
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860)
# def inference(audio):
# audio = whisper.load_audio(audio)
# audio = whisper.pad_or_trim(audio)
# mel = whisper.log_mel_spectrogram(audio).to(model.device)
# _, probs = model.detect_language(mel)
# options = whisper.DecodingOptions(fp16 = False)
# result = whisper.decode(model, mel, options)
# # print(result.text)
# return result.text, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
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