Update app.py

app.py

@@ -6,7 +6,7 @@ from transformers import WhisperProcessor, WhisperForConditionalGeneration, Whis
 nltk.download("punkt")
 from transformers import pipeline
 import scipy.io.wavfile
-
+import soundfile as sf
 
 model_name = "Shubham09/whisper31filescheck"
 processor = WhisperProcessor.from_pretrained(model_name,task="transcribe")
@@ -24,8 +24,14 @@ def load_data(input_file):
   if sample_rate !=16000:
     speech = librosa.resample(speech, sample_rate,16000)
   return speech
+    
 def write_to_file(input_file):
-    scipy.io.wavfile.write("microphone-result.wav")
+    fs = 16000
+    sf.write("my_Audio_file.flac",input_file, fs)
+
+
+    
+    #scipy.io.wavfile.write("microphone-result.wav")
     # with open("microphone-results.wav", "wb") as f:
     #     f.write(input_file.get_wav_data())
     # import base64
@@ -39,8 +45,8 @@ def write_to_file(input_file):
 #   return (' '.join([s.replace(s[0],s[0].capitalize(),1) for s in sentences]))
 pipe = pipeline(model="Shubham09/whisper31filescheck")  # change to "your-username/the-name-you-picked"
 
-def asr_transcript(input_file="microphone-result.wav"):
-    text = pipe("microphone-result.wav")["text"]
+def asr_transcript():
+    text = pipe("my_Audio_file.flac")["text"]
     return text
 
   # speech = load_data(input_file)