app.py

using tiny

app.py

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+import torch
+import gradio as gr
+import time  
+import numpy as np
+import scipy.io.wavfile
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+
+# ✅ 1️⃣ Force Model to Run on CPU
+device = "cpu"
+torch_dtype = torch.float32  # Use CPU-friendly float type
+MODEL_NAME = "openai/whisper-tiny"  # ✅ Switched to smallest model for fastest performance
+
+# ✅ 2️⃣ Load Whisper Tiny Model on CPU
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    MODEL_NAME, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+)
+model.to(device)
+
+# ✅ 3️⃣ Load Processor & Pipeline
+processor = AutoProcessor.from_pretrained(MODEL_NAME)
+
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    chunk_length_s=2,  # ✅ Process in 2-second chunks for ultra-low latency
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+# ✅ 4️⃣ Real-Time Streaming Transcription (Microphone)
+def stream_transcribe(stream, new_chunk):
+    start_time = time.time()
+    try:
+        sr, y = new_chunk
+
+        # ✅ Convert stereo to mono
+        if y.ndim > 1:
+            y = y.mean(axis=1)
+            
+        y = y.astype(np.float32)
+        y /= np.max(np.abs(y))
+
+        # ✅ Append to Stream
+        if stream is not None:
+            stream = np.concatenate([stream, y])
+        else:
+            stream = y
+            
+        # ✅ Run Transcription
+        transcription = pipe({"sampling_rate": sr, "raw": stream})["text"]
+        latency = time.time() - start_time
+
+        return stream, transcription, f"{latency:.2f} sec"
+    
+    except Exception as e:
+        print(f"Error: {e}")
+        return stream, str(e), "Error"
+
+# ✅ 5️⃣ Transcription for File Upload
+def transcribe(inputs, previous_transcription):
+    start_time = time.time()  
+    try:
+        # ✅ Convert file input to correct format
+        sample_rate, audio_data = inputs
+        transcription = pipe({"sampling_rate": sample_rate, "raw": audio_data})["text"]
+        
+        previous_transcription += transcription
+        latency = time.time() - start_time
+        
+        return previous_transcription, f"{latency:.2f} sec"
+
+    except Exception as e:
+        print(f"Error: {e}")
+        return previous_transcription, "Error"
+
+# ✅ 6️⃣ Clear Function
+def clear():
+    return ""
+
+# ✅ 7️⃣ Gradio Interface (Microphone Streaming)
+with gr.Blocks() as microphone:
+    gr.Markdown(f"# Whisper Tiny - Real-Time Transcription (CPU) 🎙️")
+    gr.Markdown(f"Using [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) for ultra-fast speech-to-text.")
+
+    with gr.Row():
+        input_audio_microphone = gr.Audio(sources=["microphone"], type="numpy", streaming=True)
+        output = gr.Textbox(label="Live Transcription", value="")
+        latency_textbox = gr.Textbox(label="Latency (seconds)", value="0.0")
+
+    with gr.Row():
+        clear_button = gr.Button("Clear Output")
+
+    state = gr.State()
+    input_audio_microphone.stream(
+        stream_transcribe, [state, input_audio_microphone], 
+        [state, output, latency_textbox], time_limit=30, stream_every=1
+    )
+    clear_button.click(clear, outputs=[output])
+
+# ✅ 8️⃣ Gradio Interface (File Upload)
+with gr.Blocks() as file:
+    gr.Markdown(f"# Upload Audio File for Transcription 🎵")
+    gr.Markdown(f"Using [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) for speech-to-text.")
+
+    with gr.Row():
+        input_audio = gr.Audio(sources=["upload"], type="numpy")
+        output = gr.Textbox(label="Transcription", value="")
+        latency_textbox = gr.Textbox(label="Latency (seconds)", value="0.0")
+
+    with gr.Row():
+        submit_button = gr.Button("Submit")
+        clear_button = gr.Button("Clear Output")
+
+    submit_button.click(transcribe, [input_audio, output], [output, latency_textbox])
+    clear_button.click(clear, outputs=[output])
+
+# ✅ 9️⃣ Final Gradio App (Supports Microphone & File Upload)
+with gr.Blocks(theme=gr.themes.Ocean()) as demo:
+    gr.TabbedInterface([microphone, file], ["Microphone", "Upload Audio"])
+
+# ✅ 1️⃣0️⃣ Run Gradio Locally
+if __name__ == "__main__":
+    demo.launch()