Update app.py

app.py

@@ -17,43 +17,15 @@ canary_model.change_decoding_strategy(decode_cfg)
 
 
 
-
-# Load the text processing model and tokenizer
-proc_tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
-proc_model = AutoModelForCausalLM.from_pretrained(
-    "microsoft/Phi-3-mini-4k-instruct",
-    trust_remote_code=True,
-)
-proc_model.eval()
-proc_model.to('cpu')
-
-
-# Load the TTS model
-tts_model = VitsModel.from_pretrained("facebook/mms-tts-eng")
-tts_tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng")
-tts_model.eval()
-tts_model.to('cpu')
-
-
-def process_speech(speech):
+def convert_speech(speech):
     # Convert the speech to text
     transcription = canary_model.transcribe(
         speech, 
         logprobs=False,
     )
 
-    # Process the text
-    inputs = proc_tokenizer.encode(transcription + proc_tokenizer.eos_token, return_tensors='pt')
-    outputs = proc_model.generate(inputs, max_length=100, temperature=0.7, pad_token_id=proc_tokenizer.eos_token_id)
-    text = proc_tokenizer.decode(outputs[0], skip_special_tokens=True)
-    processed_text = tts_tokenizer(text, return_tensors="pt")
-    
-    # Convert the processed text to speech
-    with torch.no_grad():
-        audio = tts_model(**inputs).waveform   
-
-    return audio
+    return transcription
 
-iface = gr.Interface(fn=process_speech, inputs=gr.inputs.Audio(source="microphone"), outputs="audio")
+iface = gr.Interface(fn=convert_speech, inputs=gr.inputs.Audio(source="microphone"), outputs="text")
 
 iface.launch()