gradio demo

app.py

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+from transformers import pipeline
+import gradio as gr
+from pydub import AudioSegment
+import os
+
+# Initialize the pipeline
+
+pipe = pipeline(model="srirama/whisper-small-hi")  # change to "your-username/the-name-you-picked"
+
+
+
+# Function to split audio into chunks
+def split_audio(audio_path, chunk_length_ms):
+    audio = AudioSegment.from_file(audio_path)
+    chunks = []
+    for i in range(0, len(audio), chunk_length_ms):
+        chunk = audio[i:i + chunk_length_ms]
+        chunks.append(chunk)
+    return chunks
+
+# Function to transcribe audio
+def transcribe(audio_path):
+    chunk_length_ms = 20 * 1000  # 20 seconds in milliseconds
+    audio_chunks = split_audio(audio_path, chunk_length_ms)
+    full_transcript = ""
+
+    for i, chunk in enumerate(audio_chunks):
+        temp_file = f"temp_chunk_{i}.wav"
+        chunk.export(temp_file, format="wav")
+
+        text = pipe(temp_file)["text"]
+        full_transcript += text + " "
+
+        os.remove(temp_file)  # Clean up the temporary file
+
+    return full_transcript.strip()
+
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(sources=["microphone"], type="filepath"),
+    outputs="text",
+    title="Whisper Small English",
+    description="Realtime demo for Dental speech recognition using a fine-tuned Whisper small model.",
+)
+# Launch the interface
+iface.launch()

requirements.txt

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+transformers==4.28.1
+pydub==0.25.1
+gradio==2.9.1