Create app.py

app.py

@@ -0,0 +1,144 @@
+import gradio as gr
+import time  
+import numpy as np
+import os
+import requests
+import io
+from pydub import AudioSegment
+
+
+
+def translate_audio(audio, SARVAM_API_KEY):
+
+    # API endpoint for speech-to-text translation
+    api_url = "https://api.sarvam.ai/speech-to-text-translate"
+
+    # Headers containing the API subscription key
+    headers = {
+        "api-subscription-key": SARVAM_API_KEY  # Replace with your API key
+    }
+
+    # Data payload for the translation request
+    model_data = {
+        "model": "saaras:v2",  # Specify the model to be used
+        "with_diarization": False  # Set to True for speaker diarization
+    }
+
+
+    chunk_buffer = io.BytesIO()
+    audio.export(chunk_buffer, format="wav")
+    chunk_buffer.seek(0)  # Reset the pointer to the start of the stream
+
+    # Prepare the file for the API request
+    files = {'file': ('audiofile.wav', chunk_buffer, 'audio/wav')}
+
+    try:
+        # Make the POST request to the API
+        response = requests.post(api_url, headers=headers, files=files, data=model_data)
+        if response.status_code == 200 or response.status_code == 201:
+            response_data = response.json()
+            transcript = response_data.get("transcript", "")
+        else:
+            # Handle failed requests
+            print(f"failed with status code: {response.status_code}")
+            print("Response:", response.text)
+    except Exception as e:
+        # Handle any exceptions during the request
+        print(f"Error processing chunk {e}")
+    finally:
+        # Ensure the buffer is closed after processing
+        chunk_buffer.close()
+
+    return transcript
+
+def stream_transcribe(history, new_chunk, SARVAM_API_KEY):
+    start_time = time.time()
+    
+    if history is None:
+        history = ""
+        
+    try:
+        sr, y = new_chunk
+
+        # Convert to mono if stereo
+        if y.ndim > 1:
+            y = y.mean(axis=1)
+
+        # Convert to int16 for AudioSegment
+        y_int16 = y.astype(np.int16)
+
+        # Create AudioSegment from raw PCM data
+        audio_segment = AudioSegment(
+            data=y_int16.tobytes(),
+            sample_width=2,
+            frame_rate=sr,
+            channels=1
+        )
+
+        transcription = translate_audio(audio_segment, SARVAM_API_KEY)
+        end_time = time.time()
+        latency = end_time - start_time
+        history = history + '\n' + transcription
+
+        return history, history, f"{latency:.2f}"
+    except Exception as e:
+        print(f"Error during Transcription: {e}")
+        return history, str(e), "Error"
+
+
+
+
+def clear():
+    return ""
+
+def clear_state():
+    return None
+
+def clear_api_key():
+    return ""
+
+with open("gradio.css", "r") as f:
+    custom_css = f.read()
+with gr.Blocks(theme=gr.themes.Glass()) as microphone:
+    with gr.Column():
+        
+        gr.Markdown(
+    """
+    ### 🔑 Sarvam AI API Key Required
+    To use this app, you need a free API key from [Sarvam AI](https://sarvam.ai).
+    
+    👉 **Step 1:** Visit [https://sarvam.ai](https://sarvam.ai)  
+    👉 **Step 2:** Sign up or log in  
+    👉 **Step 3:** Generate your API key and paste it below
+
+    Your key stays on your device and is not stored.
+    """
+        )
+        api_key_box = gr.Textbox(label="Enter SARVAM AI API Key", type="password")
+
+        with gr.Row():
+            input_audio_microphone = gr.Audio(streaming=True)
+            output = gr.Textbox(label="Transcription", value="")
+            latency_textbox = gr.Textbox(label="Latency (seconds)", value="0.0", scale=0)
+        with gr.Row():
+            clear_button = gr.Button("Clear Output")
+            clear_api_key_button = gr.Button("Clear API Key")
+        state = gr.State(value="")
+        def wrapped_stream_transcribe(history, new_chunk, api_key):
+            return stream_transcribe(history, new_chunk, api_key)
+        
+        input_audio_microphone.stream(
+            wrapped_stream_transcribe,
+            [state, input_audio_microphone, api_key_box],
+            [state, output, latency_textbox],
+            time_limit=30,
+            stream_every=5,
+            concurrency_limit=None,
+        )
+
+        clear_button.click(clear_state, outputs=[state]).then(clear, outputs=[output])
+        clear_api_key_button.click(clear_api_key, outputs=[api_key_box])
+
+
+demo = microphone
+demo.launch()