Create app.py

app.py

@@ -0,0 +1,58 @@
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+import gradio as gr
+from moviepy.editor import VideoFileClip
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+model_id = "distil-whisper/distil-large-v3"
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, use_safetensors=True
+)
+model.to(device)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    max_new_tokens=128,
+    chunk_length_s=25,
+    batch_size=16,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+def extract_audio_from_video(video_path, audio_output_path):
+    """Extracts audio from a video and saves it to an MP3 file."""
+    try:
+        video_clip = VideoFileClip(video_path)
+        audio_clip = video_clip.audio
+        audio_clip.write_audiofile(audio_output_path)
+        print(f"Audio extracted successfully and saved to: {audio_output_path}")
+        return audio_output_path
+    except Exception as e:
+        print(f"Error extracting audio: {e}")
+        return None
+
+def speech_to_text(input_file):
+    try:
+        if input_file.name.endswith((".mp4", ".avi", ".mov")):
+            audio_file_path = extract_audio_from_video(input_file.name, "temp_audio.mp3")
+            if audio_file_path:
+                result = pipe(audio_file_path)
+                return result[0]["transcription"]
+        else:
+            result = pipe(input_file.read())
+            return result[0]["transcription"]
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+iface = gr.Interface(fn=speech_to_text, inputs="file", outputs="text", title="Audio/Video-to-Text")
+
+if __name__ == "__main__":
+    iface.launch(debug=True)