Added in Whisper function to convert speech-to-text

app.py

@@ -1,13 +1,51 @@
-import os
-
-import numpy as np
+# import numpy as np
 
 import gradio as gr
 
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+
+# from datasets import load_dataset
+
+
+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/small"
+# 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"])
+
 
 def reverse_audio(audio):
-    sr, data = audio
-    return (sr, np.flipud(data))
+    # sr, data = audio
+    result = pipe(audio)
+    print(result["text"])
+    return result
 
 
 input_audio = gr.Audio(