Spaces:
Running
Running
File size: 9,899 Bytes
477e9f5 824b9ef 4c322cf 16a0393 4c322cf 16a0393 4c322cf 824b9ef 4c322cf 16a0393 4c322cf 16a0393 4c322cf 16a0393 4c322cf f7c5695 4c322cf f7c5695 4c322cf 16a0393 4c322cf 824b9ef 4c322cf 824b9ef 4c322cf c1f12f6 4c322cf 824b9ef 4c322cf 824b9ef 4c322cf 3b3309b 4c322cf 824b9ef 4c322cf 824b9ef f7c5695 4c322cf f7c5695 4c322cf f7c5695 4c322cf f7c5695 16a0393 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 |
# Adapted from https://github.com/SYSTRAN/faster-whisper/blob/master/faster_whisper/vad.py
from faster_whisper.vad import VadOptions, get_vad_model
import numpy as np
from typing import BinaryIO, Union, List, Optional, Tuple
import warnings
import faster_whisper
from faster_whisper.transcribe import SpeechTimestampsMap, Segment
import gradio as gr
class SileroVAD:
def __init__(self):
self.sampling_rate = 16000
self.window_size_samples = 512
self.model = None
def run(self,
audio: Union[str, BinaryIO, np.ndarray],
vad_parameters: VadOptions,
progress: gr.Progress = gr.Progress()
) -> Tuple[np.ndarray, List[dict]]:
"""
Run VAD
Parameters
----------
audio: Union[str, BinaryIO, np.ndarray]
Audio path or file binary or Audio numpy array
vad_parameters:
Options for VAD processing.
progress: gr.Progress
Indicator to show progress directly in gradio.
Returns
----------
np.ndarray
Pre-processed audio with VAD
List[dict]
Chunks of speeches to be used to restore the timestamps later
"""
sampling_rate = self.sampling_rate
if not isinstance(audio, np.ndarray):
audio = faster_whisper.decode_audio(audio, sampling_rate=sampling_rate)
duration = audio.shape[0] / sampling_rate
duration_after_vad = duration
if vad_parameters is None:
vad_parameters = VadOptions()
elif isinstance(vad_parameters, dict):
vad_parameters = VadOptions(**vad_parameters)
speech_chunks = self.get_speech_timestamps(
audio=audio,
vad_options=vad_parameters,
progress=progress
)
audio = self.collect_chunks(audio, speech_chunks)
duration_after_vad = audio.shape[0] / sampling_rate
return audio, speech_chunks
def get_speech_timestamps(
self,
audio: np.ndarray,
vad_options: Optional[VadOptions] = None,
progress: gr.Progress = gr.Progress(),
**kwargs,
) -> List[dict]:
"""This method is used for splitting long audios into speech chunks using silero VAD.
Args:
audio: One dimensional float array.
vad_options: Options for VAD processing.
kwargs: VAD options passed as keyword arguments for backward compatibility.
progress: Gradio progress to indicate progress.
Returns:
List of dicts containing begin and end samples of each speech chunk.
"""
if self.model is None:
self.update_model()
if vad_options is None:
vad_options = VadOptions(**kwargs)
threshold = vad_options.threshold
min_speech_duration_ms = vad_options.min_speech_duration_ms
max_speech_duration_s = vad_options.max_speech_duration_s
min_silence_duration_ms = vad_options.min_silence_duration_ms
window_size_samples = self.window_size_samples
speech_pad_ms = vad_options.speech_pad_ms
sampling_rate = 16000
min_speech_samples = sampling_rate * min_speech_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)
state, context = self.model.get_initial_states(batch_size=1)
speech_probs = []
for current_start_sample in range(0, audio_length_samples, window_size_samples):
progress(current_start_sample/audio_length_samples, desc="Detecting speeches only using VAD...")
chunk = audio[current_start_sample: current_start_sample + window_size_samples]
if len(chunk) < window_size_samples:
chunk = np.pad(chunk, (0, int(window_size_samples - len(chunk))))
speech_prob, state, context = self.model(chunk, state, context, sampling_rate)
speech_probs.append(speech_prob)
triggered = False
speeches = []
current_speech = {}
neg_threshold = threshold - 0.15
# to save potential segment end (and tolerate some silence)
temp_end = 0
# to save potential segment limits in case of maximum segment size reached
prev_end = next_start = 0
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 = {}
# previously reached silence (< neg_thres) and is still not speech (< thres)
if next_start < prev_end:
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
# condition to avoid cutting in very short silence
if (window_size_samples * i) - temp_end > min_silence_samples_at_max_speech:
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)
current_speech = {}
prev_end = next_start = temp_end = 0
triggered = False
continue
if (
current_speech
and (audio_length_samples - current_speech["start"]) > min_speech_samples
):
current_speech["end"] = audio_length_samples
speeches.append(current_speech)
for i, speech in enumerate(speeches):
if i == 0:
speech["start"] = int(max(0, speech["start"] - speech_pad_samples))
if i != len(speeches) - 1:
silence_duration = speeches[i + 1]["start"] - speech["end"]
if silence_duration < 2 * speech_pad_samples:
speech["end"] += int(silence_duration // 2)
speeches[i + 1]["start"] = int(
max(0, speeches[i + 1]["start"] - silence_duration // 2)
)
else:
speech["end"] = int(
min(audio_length_samples, speech["end"] + speech_pad_samples)
)
speeches[i + 1]["start"] = int(
max(0, speeches[i + 1]["start"] - speech_pad_samples)
)
else:
speech["end"] = int(
min(audio_length_samples, speech["end"] + speech_pad_samples)
)
return speeches
def update_model(self):
self.model = get_vad_model()
@staticmethod
def collect_chunks(audio: np.ndarray, chunks: List[dict]) -> np.ndarray:
"""Collects and concatenates audio chunks."""
if not chunks:
return np.array([], dtype=np.float32)
return np.concatenate([audio[chunk["start"]: chunk["end"]] for chunk in chunks])
@staticmethod
def format_timestamp(
seconds: float,
always_include_hours: bool = False,
decimal_marker: str = ".",
) -> str:
assert seconds >= 0, "non-negative timestamp expected"
milliseconds = round(seconds * 1000.0)
hours = milliseconds // 3_600_000
milliseconds -= hours * 3_600_000
minutes = milliseconds // 60_000
milliseconds -= minutes * 60_000
seconds = milliseconds // 1_000
milliseconds -= seconds * 1_000
hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
return (
f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
)
def restore_speech_timestamps(
self,
segments: List[dict],
speech_chunks: List[dict],
sampling_rate: Optional[int] = None,
) -> List[dict]:
if sampling_rate is None:
sampling_rate = self.sampling_rate
ts_map = SpeechTimestampsMap(speech_chunks, sampling_rate)
for segment in segments:
segment["start"] = ts_map.get_original_time(segment["start"])
segment["end"] = ts_map.get_original_time(segment["end"])
return segments
|