Update app.py

app.py

@@ -1,72 +1,39 @@
-# import torch
-# import torchaudio
-# import gradio as gr
-# from transformers import Wav2Vec2BertProcessor, Wav2Vec2BertForCTC
-
-# # Set device
-# device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# # Load processor & model
-# model_name = "cdactvm/w2v-bert-punjabi"  # Change if using a Punjabi ASR model
-# processor = Wav2Vec2BertProcessor.from_pretrained(model_name)
-# model = Wav2Vec2BertForCTC.from_pretrained(model_name, torch_dtype=torch.bfloat16).to(device)
-
-# def transcribe(audio_path):
-#     # Load audio file
-#     waveform, sample_rate = torchaudio.load(audio_path)
-
-#     # Convert stereo to mono (if needed)
-#     if waveform.shape[0] > 1:
-#         waveform = torch.mean(waveform, dim=0, keepdim=True)
-
-#     # Resample to 16kHz
-#     if sample_rate != 16000:
-#         waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)
-
-#     # Process audio
-#     inputs = processor(waveform.squeeze(0), sampling_rate=16000, return_tensors="pt")
-#     inputs = {key: val.to(device, dtype=torch.bfloat16) for key, val in inputs.items()}
-
-#     # Get logits & transcribe
-#     with torch.no_grad():
-#         logits = model(**inputs).logits
-#     predicted_ids = torch.argmax(logits, dim=-1)
-#     transcription = processor.batch_decode(predicted_ids)[0]
+import torch
+import torchaudio
+import gradio as gr
+from transformers import Wav2Vec2BertProcessor, Wav2Vec2BertForCTC
 
-#     return transcription
+# Set device
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# # Gradio Interface
-# app = gr.Interface(
-#     fn=transcribe,
-#     inputs=gr.Audio(sources="upload", type="filepath"),
-#     outputs="text",
-#     title="Punjabi Speech-to-Text",
-#     description="Upload an audio file and get the transcription in Punjabi."
-# )
+# Load processor & model
+model_name = "cdactvm/w2v-bert-punjabi"  # Change if using a Punjabi ASR model
+processor = Wav2Vec2BertProcessor.from_pretrained(model_name)
+model = Wav2Vec2BertForCTC.from_pretrained(model_name, torch_dtype=torch.float16).to(device)
 
-# if __name__ == "__main__":
-#     app.launch()
+def transcribe(audio_path):
+    # Load audio file
+    waveform, sample_rate = torchaudio.load(audio_path)
 
+    # Convert stereo to mono (if needed)
+    if waveform.shape[0] > 1:
+        waveform = torch.mean(waveform, dim=0, keepdim=True)
 
-import gradio as gr
-import torch
-from transformers import pipeline
+    # Resample to 16kHz
+    if sample_rate != 16000:
+        waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)
 
-# Set device
-device = "cuda" if torch.cuda.is_available() else "cpu"
+    # Process audio
+    inputs = processor(waveform.squeeze(0), sampling_rate=16000, return_tensors="pt")
+    inputs = {key: val.to(device, dtype=torch.bfloat16) for key, val in inputs.items()}
 
-# Load ASR pipeline
-asr_pipeline = pipeline(
-    "automatic-speech-recognition",
-    model="cdactvm/w2v-bert-punjabi",  # Replace with a Punjabi ASR model if available
-    torch_dtype=torch.bfloat16,
-    device=0 if torch.cuda.is_available() else -1  # GPU (0) or CPU (-1)
-)
+    # Get logits & transcribe
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.batch_decode(predicted_ids)[0]
 
-def transcribe(audio_path):
-    # Run inference
-    result = asr_pipeline(audio_path)
-    return result["text"]
+    return transcription
 
 # Gradio Interface
 app = gr.Interface(
@@ -79,3 +46,36 @@ app = gr.Interface(
 
 if __name__ == "__main__":
     app.launch()
+
+
+# import gradio as gr
+# import torch
+# from transformers import pipeline
+
+# # Set device
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# # Load ASR pipeline
+# asr_pipeline = pipeline(
+#     "automatic-speech-recognition",
+#     model="cdactvm/w2v-bert-punjabi",  # Replace with a Punjabi ASR model if available
+#     torch_dtype=torch.bfloat16,
+#     device=0 if torch.cuda.is_available() else -1  # GPU (0) or CPU (-1)
+# )
+
+# def transcribe(audio_path):
+#     # Run inference
+#     result = asr_pipeline(audio_path)
+#     return result["text"]
+
+# # Gradio Interface
+# app = gr.Interface(
+#     fn=transcribe,
+#     inputs=gr.Audio(sources="upload", type="filepath"),
+#     outputs="text",
+#     title="Punjabi Speech-to-Text",
+#     description="Upload an audio file and get the transcription in Punjabi."
+# )
+
+# if __name__ == "__main__":
+#     app.launch()