app.py

This is the optimised code

app.py

@@ -0,0 +1,115 @@
+import os
+import gc
+import torch
+import librosa
+import numpy as np
+import gradio as gr
+from transformers import (AutoProcessor, AutoModelForCTC, 
+                          AutoModelForTokenClassification, AutoTokenizer)
+from speechbrain.inference.VAD import VAD
+
+# 🔧 Check for CUDA
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# 🛠 Load Voice Activity Detection (VAD) model
+vad_model = VAD.from_hparams(source="speechbrain/vad-crdnn-libriparty", savedir="vad_model")
+
+# 🔍 Function to clean up memory
+def clean_up_memory():
+    gc.collect()
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+# 🎙 Load Wav2Vec2 ASR model
+asr_model_name = "facebook/wav2vec2-large-960h"
+processor = AutoProcessor.from_pretrained(asr_model_name)
+w2v2_model = AutoModelForCTC.from_pretrained(asr_model_name).to(device)
+w2v2_model.eval()
+
+# ✍ Load model for punctuation restoration
+recap_model_name = "kredor/punctuate-all"
+recap_tokenizer = AutoTokenizer.from_pretrained(recap_model_name)
+recap_model = AutoModelForTokenClassification.from_pretrained(recap_model_name).to(device)
+recap_model.eval()
+
+# 📌 Function to add punctuation
+def recap_sentence(string):
+    tokens = recap_tokenizer(string, return_tensors="pt", padding=True, truncation=True).to(device)
+    with torch.no_grad():
+        predictions = recap_model(**tokens).logits
+
+    predicted_ids = torch.argmax(predictions, dim=-1)[0]
+    words = string.split()
+    punctuated_text = []
+
+    for word, pred in zip(words, predicted_ids):
+        punctuated_text.append(word + recap_tokenizer.convert_ids_to_tokens([pred.item()])[0])
+
+    return " ".join(punctuated_text)
+
+# 🎧 Function for chunk-based streaming transcription
+def transcribe_audio_stream(audio_file, chunk_size=2.0):
+    audio, sr = librosa.load(audio_file, sr=16000)
+    duration = librosa.get_duration(y=audio, sr=sr)
+    transcriptions = []
+
+    for start in np.arange(0, duration, chunk_size):
+        end = min(start + chunk_size, duration)
+        chunk = audio[int(start * sr):int(end * sr)]
+        
+        input_values = processor(chunk, return_tensors="pt", sampling_rate=16000).input_values.to(w2v2_model.device)
+        
+        with torch.no_grad():
+            logits = w2v2_model(input_values).logits
+
+        predicted_ids = torch.argmax(logits, dim=-1)
+        transcription = processor.batch_decode(predicted_ids)[0]
+        transcriptions.append(transcription)
+
+    return " ".join(transcriptions)
+
+# 🎙 Handle both live audio & file uploads
+def return_prediction_w2v2(file_or_mic):
+    if not file_or_mic:
+        return "", "empty.txt"
+
+    # Transcribe file
+    transcription = transcribe_audio_stream(file_or_mic)
+
+    # Add punctuation
+    recap_result = recap_sentence(transcription)
+
+    # Save result to file
+    download_path = "transcription.txt"
+    with open(download_path, "w") as f:
+        f.write(recap_result)
+
+    clean_up_memory()
+    return recap_result, download_path
+
+# 🖥 Gradio Interface
+mic_transcribe = gr.Interface(
+    fn=return_prediction_w2v2,
+    inputs=gr.Audio(sources="microphone", type="filepath"),
+    outputs=[gr.Textbox(label="Real-Time Transcription"), gr.File(label="Download Transcript")],
+    allow_flagging="never",
+    live=True
+)
+
+file_transcribe = gr.Interface(
+    fn=return_prediction_w2v2,
+    inputs=gr.Audio(sources="upload", type="filepath"),
+    outputs=[gr.Textbox(label="File Transcription"), gr.File(label="Download Transcript")],
+    allow_flagging="never",
+    live=False
+)
+
+# 🎛 Combine into a Gradio app
+with gr.Blocks() as transcriber_app:
+    gr.Markdown("<h2>CCI Real-Time Sermon Transcription</h2>")
+    gr.TabbedInterface([mic_transcribe, file_transcribe], 
+                       ["Real-Time (Microphone)", "Upload Audio"])
+
+# 🚀 Run the Gradio app
+if __name__ == "__main__":
+    transcriber_app.launch()