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import torch
from speechbrain.inference.interfaces import Pretrained
import torchaudio
def merge_overlapping_segments(segments):
"""
Merges segments that overlap or are contiguous, ensuring each speaker segment is represented once.
Args:
segments (list of tuples): List of tuples representing (start, end, label) of segments.
Returns:
list of tuples: Merged list of segments.
"""
if not segments:
return []
merged = [segments[0]]
for current in segments[1:]:
prev = merged[-1]
if current[0] <= prev[1]:
if current[2] == prev[2]:
merged[-1] = (prev[0], max(prev[1], current[1]), prev[2])
else:
merged.append(current)
else:
merged.append(current)
return merged
def refine_transitions(aggregated_predictions):
"""
Refines transitions between speaker segments to enhance accuracy.
Args:
aggregated_predictions (list of tuples): The aggregated predictions with potential overlaps.
Returns:
list of tuples: Predictions with adjusted transitions.
"""
refined_predictions = []
for i in range(len(aggregated_predictions)):
if i == 0:
refined_predictions.append(aggregated_predictions[i])
continue
current_start, current_end, current_label = aggregated_predictions[i]
prev_start, prev_end, prev_label = aggregated_predictions[i - 1]
if current_start - prev_end <= 1.0:
new_start = prev_end
else:
new_start = current_start
refined_predictions.append((new_start, current_end, current_label))
return refined_predictions
def refine_transitions_with_confidence(aggregated_predictions, segment_confidences):
"""
Refines transitions between segments based on confidence levels.
Args:
aggregated_predictions (list of tuples): Initial aggregated predictions.
segment_confidences (list of float): Confidence scores corresponding to each segment.
Returns:
list of tuples: Refined segment predictions.
"""
refined_predictions = []
for i in range(len(aggregated_predictions)):
if i == 0:
refined_predictions.append(aggregated_predictions[i])
continue
current_start, current_end, current_label = aggregated_predictions[i]
prev_start, prev_end, prev_label, prev_confidence = refined_predictions[-1] + (segment_confidences[i - 1],)
current_confidence = segment_confidences[i]
if current_label != prev_label:
if prev_confidence < current_confidence:
transition_point = current_start
else:
transition_point = prev_end
refined_predictions[-1] = (prev_start, transition_point, prev_label)
refined_predictions.append((transition_point, current_end, current_label))
else:
if prev_confidence < current_confidence:
refined_predictions[-1] = (prev_start, current_end, current_label)
else:
refined_predictions.append((current_start, current_end, current_label))
return refined_predictions
def aggregate_segments_with_overlap(segment_predictions):
"""
Aggregates overlapping segments into single segments based on speaker labels.
Args:
segment_predictions (list of tuples): List of tuples representing (start, end, label) of segments.
Returns:
list of tuples: Aggregated segments.
"""
aggregated_predictions = []
last_start, last_end, last_label = segment_predictions[0]
for start, end, label in segment_predictions[1:]:
if label == last_label and start <= last_end:
last_end = max(last_end, end)
else:
aggregated_predictions.append((last_start, last_end, last_label))
last_start, last_end, last_label = start, end, label
aggregated_predictions.append((last_start, last_end, last_label))
merged = merge_overlapping_segments(aggregated_predictions)
return merged
class SpeakerCounter(Pretrained):
"""
A class for counting speakers in an audio file, built upon the SpeechBrain Pretrained class.
This class integrates several preprocessing and prediction modules to handle speaker diarization tasks.
"""
def __init__(self, *args, **kwargs):
"""
Initialize the SpeakerCounter with standard and custom parameters.
Args:
*args: Variable length argument list.
**kwargs: Arbitrary keyword arguments.
"""
super().__init__(*args, **kwargs)
self.sample_rate = self.hparams.sample_rate
MODULES_NEEDED = [
"compute_features",
"mean_var_norm",
"embedding_model",
"classifier",
]
def resample_waveform(self, waveform, orig_sample_rate):
"""
Resamples the input waveform to the target sample rate specified in the object.
Args:
waveform (Tensor): The input waveform tensor.
orig_sample_rate (int): The original sample rate of the waveform.
Returns:
Tensor: The resampled waveform.
"""
if orig_sample_rate != self.sample_rate:
resample_transform = torchaudio.transforms.Resample(orig_freq=orig_sample_rate, new_freq=self.sample_rate)
waveform = resample_transform(waveform)
return waveform
def encode_batch(self, wavs, wav_lens=None):
"""
Encodes a batch of waveforms into embeddings using the loaded models.
Args:
wavs (Tensor): Batch of waveforms.
wav_lens (Tensor, optional): Lengths of the waveforms for normalization.
Returns:
Tensor: Batch of embeddings.
"""
if len(wavs.shape) == 1:
wavs = wavs.unsqueeze(0)
if wav_lens is None:
wav_lens = torch.ones(wavs.shape[0], device=self.device)
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
wavs = wavs.float()
# Computing features and embeddings
feats = self.mods.compute_features(wavs)
feats = self.mods.mean_var_norm(feats, wav_lens)
embeddings = self.mods.embedding_model(feats, wav_lens)
return embeddings
def create_segments(self, waveform, segment_length, overlap):
"""
Creates segments from a single waveform for batch processing.
Args:
waveform (Tensor): Input waveform tensor.
segment_length (float): Length of each segment in seconds.
overlap (float): Overlap between segments in seconds.
Returns:
tuple: (segments, segment_times) where segments is a list of tensors, and segment_times
is a list of (start, end) times.
"""
num_samples = waveform.shape[1]
segment_samples = int(segment_length * self.sample_rate)
overlap_samples = int(overlap * self.sample_rate)
step_samples = segment_samples - overlap_samples
segments = []
segment_times = []
for start in range(0, num_samples - segment_samples + 1, step_samples):
end = start + segment_samples
segments.append(waveform[:, start:end])
start_time = start / self.sample_rate
end_time = end / self.sample_rate
segment_times.append((start_time, end_time))
return segments, segment_times
def classify_file(self, path, segment_length=2.0, overlap=1.47):
"""
Processes an audio file to classify and count speakers within segments.
Utilizes multiple stages of processing to handle overlapping speech and transitions.
Args:
path (str): Path to the audio file.
segment_length (float): Length of each segment in seconds.
overlap (float): Overlap between segments in seconds.
Outputs:
Writes the number of speakers in each segment to a text file.
"""
waveform, osr = torchaudio.load(path)
waveform = self.resample_waveform(waveform, osr)
segments, segment_times = self.create_segments(waveform, segment_length, overlap)
segment_predictions = []
for segment, (start_time, end_time) in zip(segments, segment_times):
rel_length = torch.tensor([1.0])
emb = self.encode_batch(segment, rel_length)
out_prob = self.mods.classifier(emb).squeeze(1)
score, index = torch.max(out_prob, dim=-1)
text_lab = index.item()
segment_predictions.append((start_time, end_time, text_lab))
aggregated_predictions = aggregate_segments_with_overlap(segment_predictions)
refined_predictions = refine_transitions(aggregated_predictions)
preds = refine_transitions_with_confidence(aggregated_predictions, refined_predictions)
with open("sample_segment_predictions.txt", "w") as file:
for start_time, end_time, prediction in preds:
speaker_text = "no speech" if str(prediction) == "0" else (
"1 speaker" if str(prediction) == "1" else f"{prediction} speakers")
print(f"{start_time:.2f}-{end_time:.2f} has {speaker_text}")
file.write(f"{start_time:.2f}-{end_time:.2f} has {speaker_text}\n")
def forward(self, wavs, wav_lens=None):
"""
Forward pass for classifying audio using preloaded modules.
Args:
wavs (Tensor): Input waveforms.
wav_lens (Tensor, optional): Lengths of the input waveforms.
Returns:
Output from classify_file method.
"""
return self.classify_file(wavs, wav_lens)
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