Create app.py

app.py

@@ -0,0 +1,48 @@
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+import gradio as gr
+import torchaudio
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+model_id = "distil-whisper/distil-large-v3"
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, 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=25,
+    batch_size=16,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+def speech_to_text(audio_file):
+    try:
+        waveform, sample_rate = torchaudio.load(audio_file)
+        if waveform.size(0) > 1:
+            resample = torchaudio.transforms.Resample(sample_rate, sample_rate)
+            waveform = resample(waveform)
+        waveform_np = waveform.numpy()
+        print("pass to pipe")
+        result = pipe(waveform_np[0])  
+        print("result",result)
+        return result["text"]
+    except Exception as e:
+        print(f"Error: {str(e)}")
+
+
+iface = gr.Interface(fn=speech_to_text, inputs="file", outputs="text", title="Speech-to-Text")
+
+if __name__ == "__main__":
+    iface.launch()