Update custom_interface.py

custom_interface.py

@@ -156,3 +156,67 @@ class ASR(Pretrained):
             rel_length = torch.tensor([1.0]).to(device)
             outputs = self.encode_batch(device, batch, rel_length)
             yield outputs
+
+
+    def classify_file_whisper(self, path, pipe, device):
+        waveform, sr = librosa.load(path, sr=16000)
+        transcription = pipe(waveform, generate_kwargs={"language": "macedonian"})["text"]
+        return transcription
+       
+
+    def classify_file_mms(self, path, processor, model, device):
+        # Load the audio file
+        waveform, sr = librosa.load(path, sr=16000)
+
+        # Get audio length in seconds
+        audio_length = len(waveform) / sr
+        
+        if audio_length >= 20:
+            print(f"MMS Audio is too long ({audio_length:.2f} seconds), splitting into segments")
+            # Detect non-silent segments
+            non_silent_intervals = librosa.effects.split(waveform, top_db=20)  # Adjust top_db for sensitivity
+
+            segments = []
+            current_segment = []
+            current_length = 0
+            max_duration = 20 * sr  # Maximum segment duration in samples (20 seconds)
+
+
+            for interval in non_silent_intervals:
+                start, end = interval
+                segment_part = waveform[start:end]
+
+                # If adding the next part exceeds max duration, store the segment and start a new one
+                if current_length + len(segment_part) > max_duration:
+                    segments.append(np.concatenate(current_segment))
+                    current_segment = []
+                    current_length = 0
+
+                current_segment.append(segment_part)
+                current_length += len(segment_part)
+
+            # Append the last segment if it's not empty
+            if current_segment:
+                segments.append(np.concatenate(current_segment))
+
+            # Process each segment
+            outputs = []
+            for i, segment in enumerate(segments):
+                print(f"MMS Processing segment {i + 1}/{len(segments)}, length: {len(segment) / sr:.2f} seconds")
+
+                segment_tensor = torch.tensor(segment).to(device)
+
+                # Pass the segment through the ASR model
+                inputs = processor(segment_tensor, sampling_rate=16_000, return_tensors="pt").to(device)
+                outputs = model(**inputs).logits
+                ids = torch.argmax(outputs, dim=-1)[0]
+                segment_output = processor.decode(ids)
+                yield segment_output
+        else:
+            waveform = torch.tensor(waveform).to(device)
+            inputs = processor(waveform, sampling_rate=16_000, return_tensors="pt").to(device)
+            outputs = model(**inputs).logits
+            ids = torch.argmax(outputs, dim=-1)[0]
+            transcription = processor.decode(ids)
+            yield transcription
+