Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+from pydub import AudioSegment
+import tempfile
+import torch
+import os
+
+# Set the device to CPU only
+device = "cpu"
+torch_dtype = torch.float32
+
+# Load the Whisper model and processor
+model_id = "openai/whisper-large-v3-turbo"
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+).to(device)
+processor = AutoProcessor.from_pretrained(model_id)
+
+# Create the pipeline for transcription
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+st.title("Audio/Video Transcription App")
+
+# File upload
+uploaded_file = st.file_uploader("Upload an audio or video file", type=["mp3", "wav", "mp4", "m4a"])
+
+if uploaded_file is not None:
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
+        # If it's a video, extract audio
+        if uploaded_file.name.endswith(("mp4", "m4a")):
+            audio = AudioSegment.from_file(uploaded_file)
+            audio.export(temp_audio.name, format="wav")
+        else:
+            audio = AudioSegment.from_file(uploaded_file)
+            audio.export(temp_audio.name, format="wav")
+
+        # Run the transcription
+        transcription_result = pipe(temp_audio.name, return_timestamps="word")
+
+        # Extract text and timestamps
+        transcription_text = transcription_result['text']
+        transcription_chunks = transcription_result['chunks']
+
+        # Display transcription
+        st.subheader("Transcription")
+        st.write(transcription_text)
+
+        # Generate SRT file
+        srt_content = ""
+        for i, chunk in enumerate(transcription_chunks, start=1):
+            start_time = chunk["timestamp"][0]
+            end_time = chunk["timestamp"][1]
+            text = chunk["text"]
+
+            # Format time for SRT (hours, minutes, seconds, milliseconds)
+            def format_srt_time(seconds):
+                hours, remainder = divmod(seconds, 3600)
+                minutes, seconds = divmod(remainder, 60)
+                milliseconds = int((seconds % 1) * 1000)
+                seconds = int(seconds)
+                return f"{int(hours):02}:{int(minutes):02}:{seconds:02},{milliseconds:03}"
+
+            srt_content += f"{i}\n"
+            srt_content += f"{format_srt_time(start_time)} --> {format_srt_time(end_time)}\n"
+            srt_content += f"{text}\n\n"
+
+        # Save the SRT file
+        srt_path = tempfile.mktemp(suffix=".srt")
+        with open(srt_path, "w") as srt_file:
+            srt_file.write(srt_content)
+
+        # Provide download for SRT file
+        st.subheader("Download SRT File")
+        with open(srt_path, "rb") as file:
+            st.download_button(
+                label="Download SRT",
+                data=file,
+                file_name="transcription.srt",
+                mime="text/plain"
+            )