Add application file

app.py

@@ -0,0 +1,106 @@
+import gradio as gr
+from faster_whisper import WhisperModel
+import torch
+import numpy as np
+
+import os
+import wave
+
+def model_init():
+    # get device
+    device = "cuda:0" if torch.cuda.is_available() else "cpu"
+    model_size = "large-v3"
+    if device == "cuda:0":
+        # Run on GPU with FP16
+        model = WhisperModel(model_size, device="cuda", compute_type="float16")
+        print("--------------")
+        print("Model runs on GPU")
+        print("--------------")
+        # or Run on GPU with INT8
+        # model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
+    else:
+        # Run on CPU with INT8
+        model = WhisperModel(model_size, device="cpu", compute_type="int8")
+
+    return model
+
+model = model_init()
+
+import time
+
+def transcribe_moon(stream, new_chunk):
+    start_time = time.time()  # Start timing
+    sr, y = new_chunk
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
+
+    if stream is not None:
+        stream = np.concatenate([stream, y])
+    else:
+        stream = y
+
+    # Perform the transcription using the specified model and settings
+    segments, info = model.transcribe(
+        stream,
+    )
+        # beam_size=5,
+        # vad_filter=True,
+        # vad_parameters={'min_silence_duration_ms': 500}
+    # return stream, transcriber({"sampling_rate": sr, "raw": stream})["text"]
+    # Compile the transcript with timestamps
+    transcript = "\n".join([f"[{seg.start:.2f}s -> {seg.end:.2f}s] {seg.text}" for seg in segments])
+    language_info = f"Detected language: {info.language} with probability {info.language_probability:.2f}"
+
+    end_time = time.time()  # End timing
+    execution_time = f"Execution time: {end_time - start_time:.2f} seconds"  # Calculate execution time
+
+    return stream, transcript, language_info, execution_time
+
+def transcribe(audio_file):
+    start_time = time.time()  # Start timing
+
+    # Perform the transcription using the specified model and settings
+    segments, info = model.transcribe(
+        audio_file,
+        beam_size=5,
+        vad_filter=True,
+        vad_parameters={'min_silence_duration_ms': 500}
+    )
+
+    # Compile the transcript with timestamps
+    transcript = "\n".join([f"[{seg.start:.2f}s -> {seg.end:.2f}s] {seg.text}" for seg in segments])
+    language_info = f"Detected language: {info.language} with probability {info.language_probability:.2f}"
+
+    end_time = time.time()  # End timing
+    execution_time = f"Execution time: {end_time - start_time:.2f} seconds"  # Calculate execution time
+
+    return transcript, language_info, execution_time
+
+
+# Input and Interface setup for file upload
+# , "microphone"
+input_audio = gr.Audio(sources=["upload" , "microphone"], type="filepath", label="Upload or Record Audio")
+
+file_upload_interface = gr.Interface(
+    fn=transcribe,
+    inputs=input_audio,
+     outputs=["text", "text", "text"],
+    title="Whisper Model Transcription",
+    description="Upload an MP3 file to transcribe and detect the spoken language."
+)
+
+input_audio_mic = gr.Audio(sources=["microphone"], label="Record Audio", streaming=True)
+streaming_interface = gr.Interface(
+    transcribe_moon,
+    ["state", gr.Audio(sources=["microphone"], streaming=True)],
+    ["state", "text", "text", "text"],
+    live=True,
+)
+
+# Combine both interfaces in a single Gradio app using Tabs
+tabbed_interface = gr.TabbedInterface(
+    interface_list=[file_upload_interface, streaming_interface],
+    tab_names=["Upload File", "Live Stream"]
+)
+
+tabbed_interface.launch(debug=True)