Update app.py

app.py

@@ -1,39 +1,72 @@
-import torch
-import torchaudio
-import gradio as gr
-from transformers import Wav2Vec2BertProcessor, Wav2Vec2BertForCTC
+# import torch
+# import torchaudio
+# import gradio as gr
+# from transformers import Wav2Vec2BertProcessor, Wav2Vec2BertForCTC
 
-# Set device
-device = "cuda" if torch.cuda.is_available() else "cpu"
+# # 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)
+# # 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)
+# 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]
 
-    # Convert stereo to mono (if needed)
-    if waveform.shape[0] > 1:
-        waveform = torch.mean(waveform, dim=0, keepdim=True)
+#     return transcription
 
-    # Resample to 16kHz
-    if sample_rate != 16000:
-        waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)
+# # 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."
+# )
 
-    # 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()}
+# if __name__ == "__main__":
+#     app.launch()
 
-    # 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]
 
-    return transcription
+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(