mirror of
https://github.com/gpt-omni/mini-omni
synced 2024-11-28 23:17:38 +00:00
291 lines
10 KiB
Python
291 lines
10 KiB
Python
import bisect
|
|
import functools
|
|
import os
|
|
import warnings
|
|
|
|
from typing import List, NamedTuple, Optional
|
|
|
|
import numpy as np
|
|
|
|
|
|
# The code below is adapted from https://github.com/snakers4/silero-vad.
|
|
class VadOptions(NamedTuple):
|
|
"""VAD options.
|
|
|
|
Attributes:
|
|
threshold: Speech threshold. Silero VAD outputs speech probabilities for each audio chunk,
|
|
probabilities ABOVE this value are considered as SPEECH. It is better to tune this
|
|
parameter for each dataset separately, but "lazy" 0.5 is pretty good for most datasets.
|
|
min_speech_duration_ms: Final speech chunks shorter min_speech_duration_ms are thrown out.
|
|
max_speech_duration_s: 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 100ms (if any), to prevent aggressive cutting. Otherwise, they will be
|
|
split aggressively just before max_speech_duration_s.
|
|
min_silence_duration_ms: In the end of each speech chunk wait for min_silence_duration_ms
|
|
before separating it
|
|
window_size_samples: Audio chunks of window_size_samples size are fed to the silero VAD model.
|
|
WARNING! Silero VAD models were trained using 512, 1024, 1536 samples for 16000 sample rate.
|
|
Values other than these may affect model performance!!
|
|
speech_pad_ms: Final speech chunks are padded by speech_pad_ms each side
|
|
"""
|
|
|
|
threshold: float = 0.5
|
|
min_speech_duration_ms: int = 250
|
|
max_speech_duration_s: float = float("inf")
|
|
min_silence_duration_ms: int = 2000
|
|
window_size_samples: int = 1024
|
|
speech_pad_ms: int = 400
|
|
|
|
|
|
def get_speech_timestamps(
|
|
audio: np.ndarray,
|
|
vad_options: Optional[VadOptions] = None,
|
|
**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.
|
|
|
|
Returns:
|
|
List of dicts containing begin and end samples of each speech chunk.
|
|
"""
|
|
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 = vad_options.window_size_samples
|
|
speech_pad_ms = vad_options.speech_pad_ms
|
|
|
|
if window_size_samples not in [512, 1024, 1536]:
|
|
warnings.warn(
|
|
"Unusual window_size_samples! Supported window_size_samples:\n"
|
|
" - [512, 1024, 1536] for 16000 sampling_rate"
|
|
)
|
|
|
|
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)
|
|
|
|
model = get_vad_model()
|
|
state = model.get_initial_state(batch_size=1)
|
|
|
|
speech_probs = []
|
|
for current_start_sample in range(0, audio_length_samples, window_size_samples):
|
|
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 = model(chunk, state, 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 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])
|
|
|
|
|
|
class SpeechTimestampsMap:
|
|
"""Helper class to restore original speech timestamps."""
|
|
|
|
def __init__(self, chunks: List[dict], sampling_rate: int, time_precision: int = 2):
|
|
self.sampling_rate = sampling_rate
|
|
self.time_precision = time_precision
|
|
self.chunk_end_sample = []
|
|
self.total_silence_before = []
|
|
|
|
previous_end = 0
|
|
silent_samples = 0
|
|
|
|
for chunk in chunks:
|
|
silent_samples += chunk["start"] - previous_end
|
|
previous_end = chunk["end"]
|
|
|
|
self.chunk_end_sample.append(chunk["end"] - silent_samples)
|
|
self.total_silence_before.append(silent_samples / sampling_rate)
|
|
|
|
def get_original_time(
|
|
self,
|
|
time: float,
|
|
chunk_index: Optional[int] = None,
|
|
) -> float:
|
|
if chunk_index is None:
|
|
chunk_index = self.get_chunk_index(time)
|
|
|
|
total_silence_before = self.total_silence_before[chunk_index]
|
|
return round(total_silence_before + time, self.time_precision)
|
|
|
|
def get_chunk_index(self, time: float) -> int:
|
|
sample = int(time * self.sampling_rate)
|
|
return min(
|
|
bisect.bisect(self.chunk_end_sample, sample),
|
|
len(self.chunk_end_sample) - 1,
|
|
)
|
|
|
|
|
|
@functools.lru_cache
|
|
def get_vad_model():
|
|
"""Returns the VAD model instance."""
|
|
asset_dir = os.path.join(os.path.dirname(__file__), "assets")
|
|
path = os.path.join(asset_dir, "silero_vad.onnx")
|
|
return SileroVADModel(path)
|
|
|
|
|
|
class SileroVADModel:
|
|
def __init__(self, path):
|
|
try:
|
|
import onnxruntime
|
|
except ImportError as e:
|
|
raise RuntimeError(
|
|
"Applying the VAD filter requires the onnxruntime package"
|
|
) from e
|
|
|
|
opts = onnxruntime.SessionOptions()
|
|
opts.inter_op_num_threads = 1
|
|
opts.intra_op_num_threads = 1
|
|
opts.log_severity_level = 4
|
|
|
|
self.session = onnxruntime.InferenceSession(
|
|
path,
|
|
providers=["CPUExecutionProvider"],
|
|
sess_options=opts,
|
|
)
|
|
|
|
def get_initial_state(self, batch_size: int):
|
|
h = np.zeros((2, batch_size, 64), dtype=np.float32)
|
|
c = np.zeros((2, batch_size, 64), dtype=np.float32)
|
|
return h, c
|
|
|
|
def __call__(self, x, state, sr: int):
|
|
if len(x.shape) == 1:
|
|
x = np.expand_dims(x, 0)
|
|
if len(x.shape) > 2:
|
|
raise ValueError(
|
|
f"Too many dimensions for input audio chunk {len(x.shape)}"
|
|
)
|
|
if sr / x.shape[1] > 31.25:
|
|
raise ValueError("Input audio chunk is too short")
|
|
|
|
h, c = state
|
|
|
|
ort_inputs = {
|
|
"input": x,
|
|
"h": h,
|
|
"c": c,
|
|
"sr": np.array(sr, dtype="int64"),
|
|
}
|
|
|
|
out, h, c = self.session.run(None, ort_inputs)
|
|
state = (h, c)
|
|
|
|
return out, state
|