1rst sortformer backend implementation

whisperlivekit/diarization/sortformer_backend.py

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+import numpy as np
+import torch
+import logging
+
+logger = logging.getLogger(__name__)
+
+try:
+    from nemo.collections.asr.models import SortformerEncLabelModel
+except ImportError:
+    raise SystemExit("""Please use `pip install "git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[asr]"` to use the Sortformer diarization""")
+    
+
+diar_model = SortformerEncLabelModel.from_pretrained("nvidia/diar_streaming_sortformer_4spk-v2")
+diar_model.eval()
+
+if torch.cuda.is_available():
+    diar_model.to(torch.device("cuda"))
+    
+# Set the streaming parameters corresponding to 1.04s latency setup. This will affect the streaming feat loader.
+# diar_model.sortformer_modules.chunk_len = 6
+# diar_model.sortformer_modules.spkcache_len = 188
+# diar_model.sortformer_modules.chunk_right_context = 7
+# diar_model.sortformer_modules.fifo_len = 188
+# diar_model.sortformer_modules.spkcache_update_period = 144
+# diar_model.sortformer_modules.log = False
+
+
+# here we change the settings for our goal: speed!
+# we want batches of around 1 second. one frame is 0.08s, so 1s is 12.5 frames. we take 12.
+diar_model.sortformer_modules.chunk_len = 12
+
+# for more speed, we reduce the 'right context'. it's like looking less into the future.
+diar_model.sortformer_modules.chunk_right_context = 1
+
+# we keep the rest same for now
+diar_model.sortformer_modules.spkcache_len = 188
+diar_model.sortformer_modules.fifo_len = 188
+diar_model.sortformer_modules.spkcache_update_period = 144
+diar_model.sortformer_modules.log = False
+diar_model.sortformer_modules._check_streaming_parameters()
+
+batch_size = 1
+processed_signal_offset = torch.zeros((batch_size,), dtype=torch.long, device=diar_model.device)
+
+def prepare_audio_signal(signal):
+    audio_signal = torch.tensor(signal).unsqueeze(0).to(diar_model.device)
+    audio_signal_length = torch.tensor([audio_signal.shape[1]]).to(diar_model.device)
+    processed_signal, processed_signal_length = diar_model.preprocessor(input_signal=audio_signal, length=audio_signal_length)
+    return processed_signal, processed_signal_length
+
+def create_streaming_loader(processed_signal, processed_signal_length):
+    streaming_loader = diar_model.sortformer_modules.streaming_feat_loader(
+        feat_seq=processed_signal,
+        feat_seq_length=processed_signal_length,
+        feat_seq_offset=processed_signal_offset,
+    )
+    return streaming_loader
+
+
+def process_diarization(streaming_loader):
+    
+    streaming_state = diar_model.sortformer_modules.init_streaming_state(
+        batch_size = batch_size,
+        async_streaming = True,
+        device = diar_model.device
+)
+    total_preds = torch.zeros((batch_size, 0, diar_model.sortformer_modules.n_spk), device=diar_model.device)
+
+    
+    chunk_duration_seconds = diar_model.sortformer_modules.chunk_len * diar_model.sortformer_modules.subsampling_factor * diar_model.preprocessor._cfg.window_stride
+    print(f"Chunk duration: {chunk_duration_seconds} seconds")
+
+    l_speakers = [
+        {'start_time': 0,
+        'end_time': 0,
+        'speaker': 0
+        }
+    ]
+    len_prediction = None
+    for i, chunk_feat_seq_t, feat_lengths, left_offset, right_offset in streaming_loader:
+        with torch.inference_mode():
+                streaming_state, total_preds = diar_model.forward_streaming_step(
+                    processed_signal=chunk_feat_seq_t,
+                    processed_signal_length=feat_lengths,
+                    streaming_state=streaming_state,
+                    total_preds=total_preds,
+                    left_offset=left_offset,
+                    right_offset=right_offset,
+                )
+                preds_np = total_preds[0].cpu().numpy()
+                active_speakers = np.argmax(preds_np, axis=1)
+                if len_prediction is None:
+                    len_prediction = len(active_speakers) # we want to get the len of 1 prediction
+                frame_duration = chunk_duration_seconds / len_prediction
+                active_speakers = active_speakers[-len_prediction:]
+
+                for idx, spk in enumerate(active_speakers):
+                    if spk != l_speakers[-1]['speaker']:
+                        l_speakers.append(
+                            {'start_time': i * chunk_duration_seconds + idx * frame_duration,
+                            'end_time': i * chunk_duration_seconds + (idx + 1) * frame_duration,
+                            'speaker': spk
+                        })                    
+                    else:
+                        l_speakers[-1]['end_time'] = i * chunk_duration_seconds + (idx + 1) * frame_duration
+                    
+                print(l_speakers)
+
+if __name__ == '__main__':
+    import librosa
+    an4_audio = 'new_audio_test.mp3'
+    signal, sr = librosa.load(an4_audio,sr=16000) 
+
+
+    processed_signal, processed_signal_length = prepare_audio_signal(signal)
+    streaming_loader = create_streaming_loader(processed_signal, processed_signal_length)
+    process_diarization(streaming_loader)