initial version of a flask sever serving deep speech model

demo/server.py

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+import torch
+import os
+from tempfile import NamedTemporaryFile
+import subprocess
+from flask import Flask, request, jsonify
+from torch.autograd import Variable
+from data.data_loader import SpectrogramParser
+from decoder import GreedyDecoder
+from model import DeepSpeech
+
+
+app = Flask(__name__)
+ALLOWED_EXTENSIONS = set(['.wav', '.mp3', '.ogg', '.webm'])
+
+speech_transcriber = None
+
+class SpeechTranscriber:
+ def __init__(self, model_path):
+ """
+
+ :param model_path:
+ """
+ assert os.path.exists(model_path), "Cannot find model here {}".format(model_path)
+ self.deep_speech_model = DeepSpeech.load_model(model_path)
+ self.deep_speech_model.eval()
+ labels = DeepSpeech.get_labels(self.deep_speech_model)
+ self.audio_conf = DeepSpeech.get_audio_conf(self.deep_speech_model)
+ self.decoder = GreedyDecoder(labels)
+ self.parser = SpectrogramParser(self.audio_conf, normalize=True)
+
+ def transcribe(self, audio_file):
+ """
+
+ :param audio_file:
+ :return:
+ """
+ spect = self.parser.parse_audio(audio_file).contiguous()
+ spect = spect.view(1, 1, spect.size(0), spect.size(1))
+ out = self.deep_speech_model(Variable(spect, volatile=True))
+ out = out.transpose(0, 1) # TxNxH
+ decoded_output = self.decoder.decode(out.data)
+ return decoded_output
+
+
+@app.route('/transcribe', methods=['POST'])
+def transcribe_file():
+ """
+
+ :return:
+ """
+ res = {}
+ if request.method == 'POST':
+ if 'file' not in request.files:
+ res['status'] = "error"
+ res['message'] = "audio file should be passed for the transcription"
+ return jsonify(res)
+ file = request.files['file']
+ filename = file.filename
+ _, file_extension = os.path.splitext(filename)
+ if file_extension.lower() not in ALLOWED_EXTENSIONS:
+ res['status'] = "error"
+ res['message'] = "{} is not supported format.".format(file_extension)
+ return jsonify(res)
+ with NamedTemporaryFile(suffix=file_extension) as tmp_saved_audio_file:
+ file.save( tmp_saved_audio_file.name )
+ target_file = tmp_saved_audio_file.name.replace(file_extension, '_converted.wav')
+ with open(os.devnull, 'w') as devnull:
+ subprocess.call(["ffmpeg", '-i', tmp_saved_audio_file.name, "-ar", '16000', "-ab", "32", target_file],
+ stdout=devnull, stderr=devnull)
+ transcription = speech_transcriber.transcribe(target_file)[0]
+ res['status'] = "OK"
+ res['transcription'] = transcription
+ return jsonify(res)
+
+def main():
+ import argparse
+ parser = argparse.ArgumentParser(description='DeepSpeech transcription server')
+ parser.add_argument('--model_path', default='./../models/deepspeech_final.pth.tar',
+ help='Path to model file created by training')
+ parser.add_argument('--port', type=int, default=8888, help='Port to be used by the server')
+ opt = parser.parse_args()
+
+ global speech_transcriber
+ speech_transcriber = SpeechTranscriber(model_path=opt.model_path)
+ app.run(host='0.0.0.0',
+ port=opt.port, debug=True, use_reloader=False,)
+
+if __name__ == "__main__":
+ main()