Create diarization_utils

diarization_utils

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+import torch
+import numpy as np
+from torchaudio import functional as F
+from transformers.pipelines.audio_utils import ffmpeg_read
+from starlette.exceptions import HTTPException
+import sys
+
+# Code from insanely-fast-whisper:
+# https://github.com/Vaibhavs10/insanely-fast-whisper
+
+import logging
+logger = logging.getLogger(__name__)
+
+def preprocess_inputs(inputs, sampling_rate):
+    inputs = ffmpeg_read(inputs, sampling_rate)
+
+    if sampling_rate != 16000:
+        inputs = F.resample(
+            torch.from_numpy(inputs), sampling_rate, 16000
+        ).numpy()
+
+    if len(inputs.shape) != 1:
+        logger.error(f"Diarization pipeline expecs single channel audio, received {inputs.shape}")
+        raise HTTPException(
+            status_code=400,
+            detail=f"Diarization pipeline expecs single channel audio, received {inputs.shape}"
+        )
+
+    # diarization model expects float32 torch tensor of shape `(channels, seq_len)`
+    diarizer_inputs = torch.from_numpy(inputs).float()
+    diarizer_inputs = diarizer_inputs.unsqueeze(0)
+
+    return inputs, diarizer_inputs
+
+
+def diarize_audio(diarizer_inputs, diarization_pipeline, parameters):
+    diarization = diarization_pipeline(
+        {"waveform": diarizer_inputs, "sample_rate": parameters.sampling_rate},
+        num_speakers=parameters.num_speakers,
+        min_speakers=parameters.min_speakers,
+        max_speakers=parameters.max_speakers,
+    )
+
+    segments = []
+    for segment, track, label in diarization.itertracks(yield_label=True):
+        segments.append(
+            {
+                "segment": {"start": segment.start, "end": segment.end},
+                "track": track,
+                "label": label,
+            }
+        )
+
+    # diarizer output may contain consecutive segments from the same speaker (e.g. {(0 -> 1, speaker_1), (1 -> 1.5, speaker_1), ...})
+    # we combine these segments to give overall timestamps for each speaker's turn (e.g. {(0 -> 1.5, speaker_1), ...})
+    new_segments = []
+    prev_segment = cur_segment = segments[0]
+
+    for i in range(1, len(segments)):
+        cur_segment = segments[i]
+
+        # check if we have changed speaker ("label")
+        if cur_segment["label"] != prev_segment["label"] and i < len(segments):
+            # add the start/end times for the super-segment to the new list
+            new_segments.append(
+                {
+                    "segment": {
+                        "start": prev_segment["segment"]["start"],
+                        "end": cur_segment["segment"]["start"],
+                    },
+                    "speaker": prev_segment["label"],
+                }
+            )
+            prev_segment = segments[i]
+
+    # add the last segment(s) if there was no speaker change
+    new_segments.append(
+        {
+            "segment": {
+                "start": prev_segment["segment"]["start"],
+                "end": cur_segment["segment"]["end"],
+            },
+            "speaker": prev_segment["label"],
+        }
+    )
+
+    return new_segments
+
+
+def post_process_segments_and_transcripts(new_segments, transcript, group_by_speaker) -> list:
+    # get the end timestamps for each chunk from the ASR output
+    end_timestamps = np.array(
+        [chunk["timestamp"][-1] if chunk["timestamp"][-1] is not None else sys.float_info.max for chunk in transcript])
+    segmented_preds = []
+
+    # align the diarizer timestamps and the ASR timestamps
+    for segment in new_segments:
+        # get the diarizer end timestamp
+        end_time = segment["segment"]["end"]
+        # find the ASR end timestamp that is closest to the diarizer's end timestamp and cut the transcript to here
+        upto_idx = np.argmin(np.abs(end_timestamps - end_time))
+
+        if group_by_speaker:
+            segmented_preds.append(
+                {
+                    "speaker": segment["speaker"],
+                    "text": "".join(
+                        [chunk["text"] for chunk in transcript[: upto_idx + 1]]
+                    ),
+                    "timestamp": (
+                        transcript[0]["timestamp"][0],
+                        transcript[upto_idx]["timestamp"][1],
+                    ),
+                }
+            )
+        else:
+            for i in range(upto_idx + 1):
+                segmented_preds.append({"speaker": segment["speaker"], **transcript[i]})
+
+        # crop the transcripts and timestamp lists according to the latest timestamp (for faster argmin)
+        transcript = transcript[upto_idx + 1:]
+        end_timestamps = end_timestamps[upto_idx + 1:]
+
+        if len(end_timestamps) == 0:
+            break
+
+    return segmented_preds
+
+
+def diarize(diarization_pipeline, file, parameters, asr_outputs):
+    _, diarizer_inputs = preprocess_inputs(file, parameters.sampling_rate)
+
+    segments = diarize_audio(
+        diarizer_inputs, 
+        diarization_pipeline, 
+        parameters
+    )
+
+    return post_process_segments_and_transcripts(
+        segments, asr_outputs["chunks"], group_by_speaker=False
+    )