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| import os | |
| os.system("pip install git+https://github.com/sanchit-gandhi/whisper-jax.git") | |
| # import whisper | |
| from flask import Flask, jsonify, request | |
| import requests | |
| import time | |
| # from transformers import pipeline | |
| from whisper_jax import FlaxWhisperPipline | |
| import jax.numpy as jnp | |
| # model = whisper.load_model("small") | |
| # pipe = pipeline( | |
| # "automatic-speech-recognition", | |
| # model="openai/whisper-small.en", | |
| # chunk_length_s=15, | |
| # device=model.device, | |
| # ) | |
| pipe = FlaxWhisperPipline("openai/whisper-small", dtype=jnp.bfloat16, batch_size=16) | |
| app = Flask(__name__) | |
| app.config['TIMEOUT'] = 60 * 10 # 10 mins | |
| def indexApi(): | |
| return jsonify({"output": "okay"}) | |
| 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(audio, task="transcribe")["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) | |