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Merge pull request #271 from b-med/max_speech_duration_v4

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Max speech duration + bits per sample
This commit is contained in:
Dimitrii Voronin
2022-12-06 11:29:15 +02:00
committed by GitHub
parent 82d199ff22 ab7af9745b
commit 42565d5baa
1 changed files with 36 additions and 4 deletions
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40
utils_vad.py
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@@ -137,7 +137,7 @@ def read_audio(path: str,
def save_audio(path: str,
tensor: torch.Tensor,
sampling_rate: int = 16000):
torchaudio.save(path, tensor.unsqueeze(0), sampling_rate)
torchaudio.save(path, tensor.unsqueeze(0), sampling_rate, bits_per_sample=16)
def init_jit_model(model_path: str,
@@ -163,6 +163,7 @@ def get_speech_timestamps(audio: torch.Tensor,
threshold: float = 0.5,
sampling_rate: int = 16000,
min_speech_duration_ms: int = 250,
max_speech_duration_s: float = float('inf'),
min_silence_duration_ms: int = 100,
window_size_samples: int = 512,
speech_pad_ms: int = 30,
@@ -189,6 +190,11 @@ def get_speech_timestamps(audio: torch.Tensor,
min_speech_duration_ms: int (default - 250 milliseconds)
Final speech chunks shorter min_speech_duration_ms are thrown out
max_speech_duration_s: int (default - inf)
Maximum duration of speech chunks in seconds
Chunks longer than max_speech_duration_s will be split at the timestamp of the last silence that lasts more than 100s (if any), to prevent agressive cutting.
Otherwise, they will be split aggressively just before max_speech_duration_s.
min_silence_duration_ms: int (default - 100 milliseconds)
In the end of each speech chunk wait for min_silence_duration_ms before separating it
@@ -239,8 +245,10 @@ def get_speech_timestamps(audio: torch.Tensor,
model.reset_states()
min_speech_samples = sampling_rate * min_speech_duration_ms / 1000
min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
speech_pad_samples = sampling_rate * speech_pad_ms / 1000
max_speech_samples = sampling_rate * max_speech_duration_s - window_size_samples - 2 * speech_pad_samples
min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
min_silence_samples_at_max_speech = sampling_rate * 98 / 1000
audio_length_samples = len(audio)
@@ -256,28 +264,52 @@ def get_speech_timestamps(audio: torch.Tensor,
speeches = []
current_speech = {}
neg_threshold = threshold - 0.15
temp_end = 0
temp_end = 0 # to save potential segment end (and tolerate some silence)
prev_end = next_start = 0 # to save potential segment limits in case of maximum segment size reached
for i, speech_prob in enumerate(speech_probs):
if (speech_prob >= threshold) and temp_end:
temp_end = 0
if next_start < prev_end:
next_start = window_size_samples * i
if (speech_prob >= threshold) and not triggered:
triggered = True
current_speech['start'] = window_size_samples * i
continue
if triggered and (window_size_samples * i) - current_speech['start'] > max_speech_samples:
if prev_end:
current_speech['end'] = prev_end
speeches.append(current_speech)
current_speech = {}
if next_start < prev_end: # previously reached silence (< neg_thres) and is still not speech (< thres)
triggered = False
else:
current_speech['start'] = next_start
prev_end = next_start = temp_end = 0
else:
current_speech['end'] = window_size_samples * i
speeches.append(current_speech)
current_speech = {}
prev_end = next_start = temp_end = 0
triggered = False
continue
if (speech_prob < neg_threshold) and triggered:
if not temp_end:
temp_end = window_size_samples * i
if ((window_size_samples * i) - temp_end) > min_silence_samples_at_max_speech : # condition to avoid cutting in very short silence
prev_end = temp_end
if (window_size_samples * i) - temp_end < min_silence_samples:
continue
else:
current_speech['end'] = temp_end
if (current_speech['end'] - current_speech['start']) > min_speech_samples:
speeches.append(current_speech)
temp_end = 0
current_speech = {}
prev_end = next_start = temp_end = 0
triggered = False
continue