add openai whisper-large-v3

app.py

@@ -1,10 +1,44 @@
 import streamlit as st
 from st_audiorec import st_audiorec
 
+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')
+    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"])