Create app.py

app.py

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+import torch
+import torchaudio
+import gradio as gr
+import time
+import numpy as np
+import scipy.io.wavfile
+
+# ✅ 1️⃣ Load Silero STT Model for CPU
+device = torch.device("cpu")  # ✅ Ensuring CPU-only execution
+torch_dtype = torch.float32
+
+MODEL_NAME = "silero_stt"
+
+# ✅ 2️⃣ Load Silero Model & Decoder
+torch.set_num_threads(4)  # ✅ Improve CPU performance by using multiple threads
+model, decoder, utils = torch.hub.load(repo_or_dir="snakers4/silero-models",
+                                       model="silero_stt",
+                                       language="en",
+                                       device=device)
+(read_batch, split_into_batches, read_audio, prepare_model_input) = utils
+
+# ✅ 3️⃣ 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))
+
+        # ✅ Resample audio to 16kHz using torchaudio
+        y_tensor = torch.tensor(y)
+        y_resampled = torchaudio.functional.resample(y_tensor, orig_freq=sr, new_freq=16000).numpy()
+
+        # ✅ Append to Stream
+        if stream is not None:
+            stream = np.concatenate([stream, y_resampled])
+        else:
+            stream = y_resampled
+
+        # ✅ Prepare Model Input
+        input_tensor = torch.from_numpy(stream).unsqueeze(0)
+        input_tensor = prepare_model_input(input_tensor, device=device)
+
+        # ✅ Run Transcription
+        transcription = model(input_tensor)
+        text = decoder(transcription[0].cpu())
+
+        latency = time.time() - start_time
+        return stream, text, f"{latency:.2f} sec"
+
+    except Exception as e:
+        print(f"Error: {e}")
+        return stream, str(e), "Error"
+
+# ✅ 4️⃣ 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
+
+        # ✅ Resample using torchaudio (optimized)
+        audio_tensor = torch.tensor(audio_data)
+        resampled_audio = torchaudio.functional.resample(audio_tensor, orig_freq=sample_rate, new_freq=16000).numpy()
+
+        # ✅ Prepare Model Input
+        input_tensor = torch.from_numpy(resampled_audio).unsqueeze(0)
+        input_tensor = prepare_model_input(input_tensor, device=device)
+
+        # ✅ Run Transcription
+        transcription = model(input_tensor)
+        text = decoder(transcription[0].cpu())
+
+        previous_transcription += text
+        latency = time.time() - start_time
+
+        return previous_transcription, f"{latency:.2f} sec"
+
+    except Exception as e:
+        print(f"Error: {e}")
+        return previous_transcription, "Error"
+
+# ✅ 5️⃣ Clear Function
+def clear():
+    return ""
+
+# ✅ 6️⃣ Gradio Interface (Microphone Streaming)
+with gr.Blocks() as microphone:
+    gr.Markdown(f"# Silero STT - Real-Time Transcription (Optimized CPU) 🎙️")
+    gr.Markdown("Using `Silero STT` for lightweight, accurate 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])
+
+# ✅ 7️⃣ Gradio Interface (File Upload)
+with gr.Blocks() as file:
+    gr.Markdown(f"# Upload Audio File for Transcription 🎵")
+    gr.Markdown("Using `Silero STT` for offline, high-accuracy transcription.")
+
+    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])
+
+# ✅ 8️⃣ Final Gradio App
+with gr.Blocks(theme=gr.themes.Ocean()) as demo:
+    gr.TabbedInterface([microphone, file], ["Microphone", "Upload Audio"])
+
+# ✅ 9️⃣ Run Gradio Locally
+if __name__ == "__main__":
+    demo.launch()