save file

nemo_asr.py

@@ -4,19 +4,23 @@ import nemo.collections.asr as nemo_asr
 
 
 def transcribe(file, modelName="stt_rw_conformer_transducer_large"):
-  with open(file.name, 'rb') as out_file:
-    out_file = file.read()  # async read
+  with open(file.name, 'wb') as out_file:
+    #out_file = file.read()  # async read
+    content = file.read()  
+    out_file.write(content)
     #out_file.write(content)  # async write
-    print(out_file.name)
+    #print(out_file.name)
   asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(
                 model_name=modelName)
-  if not file.name.endswith("wav"):
-    sound = AudioSegment.from_mp3(out_file.name)
-    sound.export(out_file.name, format="wav")
-    files = [out_file.name]
-  pac.convert_wav_to_16bit_mono(out_file.name,out_file.name)
+#   if not file.name.endswith("wav"):
+#     sound = AudioSegment.from_mp3(out_file.name)
+#     sound.export(out_file.name, format="wav")
+#     files = [out_file.name]
+#   pac.convert_wav_to_16bit_mono(out_file.name,out_file.name)
     # print("file loaded is **************",file.file)
-  for fname, transcription in zip(files, asr_model.transcribe(paths2audio_files=files)):
-    print(f"Audio in {fname} was recognized as: {transcription}")
-    print(transcription[0])
-    return {"text": transcription[0], "filename": file.filename}
+#   for fname, transcription in zip(files, asr_model.transcribe(paths2audio_files=files)):
+#     print(f"Audio in {fname} was recognized as: {transcription}")
+#     print(transcription[0])
+  transcription= asr_model.transcribe([out_file.name])
+  print(transcription)
+  return {"text": transcription, "filename": out_file.name}