update app.py add transcribe function

app.py

@@ -5,40 +5,60 @@ from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
 from datasets import load_dataset
 import torch
 
-# x = st.slider('Select a value')
-# st.write(x, 'squared is', x * x)
-
-wav_audio_data = st_audiorec()
-
-if wav_audio_data is not None:
-    st.audio(wav_audio_data, format='audio/wav')
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-
-model_id = "openai/whisper-large-v3"
-
-model = AutoModelForSpeechSeq2Seq.from_pretrained(
-    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
-)
-model.to(device)
-
-processor = AutoProcessor.from_pretrained(model_id)
-
-pipe = pipeline(
-    "automatic-speech-recognition",
-    model=model,
-    tokenizer=processor.tokenizer,
-    feature_extractor=processor.feature_extractor,
-    max_new_tokens=128,
-    chunk_length_s=30,
-    batch_size=16,
-    return_timestamps=True,
-    torch_dtype=torch_dtype,
-    device=device,
-)
-
-dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
-sample = dataset[0]["audio"]
-result = pipe(sample)
-print(result["text"])
+pipe = None
+audio_sample: bytes = None
+audio_transcription: str = None
+
+def main ():
+    
+    init_model()
+    # x = st.slider('Select a value')
+    # st.write(x, 'squared is', x * x)
+
+    wav_audio_data = st_audiorec()
+
+    if wav_audio_data is not None:
+        st.audio(wav_audio_data, format='audio/wav')
+
+    dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
+    sample = dataset[0]["audio"]
+    st.write('Sample:', sample)
+
+async def init_model ():
+    device = "cuda:0" if torch.cuda.is_available() else "cpu"
+    torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+    model_id = "openai/whisper-large-v3"
+
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+    )
+    model.to(device)
+
+    processor = AutoProcessor.from_pretrained(model_id)
+
+    pipe = pipeline(
+        "automatic-speech-recognition",
+        model=model,
+        tokenizer=processor.tokenizer,
+        feature_extractor=processor.feature_extractor,
+        max_new_tokens=128,
+        chunk_length_s=30,
+        batch_size=16,
+        return_timestamps=True,
+        torch_dtype=torch_dtype,
+        device=device,
+    )
+
+async def transcribe (audio_sample: bytes, pipe) -> str:
+
+    # dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
+    # sample = dataset[0]["audio"]
+    result = pipe(audio_sample)
+    print(result)
+    
+    st.write('Result', result["text"])
+    return result["text"]
+
+if __name__ == "__main__":
+    main()