transcribe/helpers/vadprocessor.py

from copy import deepcopy
from time import time
from config import VAD_MODEL_PATH
from silero_vad import load_silero_vad
import numpy as np
import onnxruntime
import logging
from datetime import timedelta
import gc
from pydub import AudioSegment
from collections import deque

class AdaptiveSilenceController:
    def __init__(self, base_silence_ms=120, min_ms=50, max_ms=600):
        self.base = base_silence_ms
        self.min = min_ms
        self.max = max_ms
        self.recent_silences = deque(maxlen=20)
        self.recent_speeches = deque(maxlen=20)

    def update_silence(self, duration_ms):
        self.recent_silences.append(duration_ms)

    def update_speech(self, duration_ms):
        self.recent_speeches.append(duration_ms)

    def get_adaptive_silence_ms(self):
        # 1. 快速说话特征：平均语音段长度短（如 < 250ms）
        avg_speech = np.mean(self.recent_speeches) if self.recent_speeches else self.base
        avg_silence = np.mean(self.recent_silences) if self.recent_silences else self.base

        # 2. 快速语音则缩短 silence 阈值
        speed_factor = 1.0
        if avg_speech < 300:
            speed_factor = 0.5
        elif avg_speech < 600:
            speed_factor = 0.8
        logging.warning(f"Avg speech :{avg_speech}, Avg silence: {avg_silence}")
        # 3. silence 的变化趋势也考虑进去
        adaptive = self.base * speed_factor + 0.3 * avg_silence

        return int(max(self.min, min(self.max, adaptive)))

    
class OnnxWrapper():

    def __init__(self, path, force_onnx_cpu=False):
        opts = onnxruntime.SessionOptions()
        opts.inter_op_num_threads = 1
        opts.intra_op_num_threads = 1

        if force_onnx_cpu and 'CPUExecutionProvider' in onnxruntime.get_available_providers():
            self.session = onnxruntime.InferenceSession(path, providers=['CPUExecutionProvider'], sess_options=opts)
        else:
            self.session = onnxruntime.InferenceSession(path, sess_options=opts)

        self.reset_states()
        self.sample_rates = [16000]

    def _validate_input(self, x: np.ndarray, sr: int):
        if x.ndim == 1:
            x = x[None]
        if x.ndim > 2:
            raise ValueError(f"Too many dimensions for input audio chunk {x.ndim}")

        if sr != 16000 and (sr % 16000 == 0):
            step = sr // 16000
            x = x[:, ::step]
            sr = 16000

        if sr not in self.sample_rates:
            raise ValueError(f"Supported sampling rates: {self.sample_rates} (or multiply of 16000)")
        if sr / x.shape[1] > 31.25:
            raise ValueError("Input audio chunk is too short")

        return x, sr

    def reset_states(self, batch_size=1):
        self._state = np.zeros((2, batch_size, 128)).astype(np.float32)
        self._context = np.zeros(0)
        self._last_sr = 0
        self._last_batch_size = 0

    def __call__(self, x, sr: int):

        x, sr = self._validate_input(x, sr)
        num_samples = 512 if sr == 16000 else 256

        if x.shape[-1] != num_samples:
            raise ValueError(
                f"Provided number of samples is {x.shape[-1]} (Supported values: 256 for 8000 sample rate, 512 for 16000)")

        batch_size = x.shape[0]
        context_size = 64 if sr == 16000 else 32

        if not self._last_batch_size:
            self.reset_states(batch_size)
        if (self._last_sr) and (self._last_sr != sr):
            self.reset_states(batch_size)
        if (self._last_batch_size) and (self._last_batch_size != batch_size):
            self.reset_states(batch_size)

        if not len(self._context):
            self._context = np.zeros((batch_size, context_size)).astype(np.float32)

        x = np.concatenate([self._context, x], axis=1)
        if sr in [8000, 16000]:
            ort_inputs = {'input': x, 'state': self._state, 'sr': np.array(sr, dtype='int64')}
            ort_outs = self.session.run(None, ort_inputs)
            out, state = ort_outs
            self._state = state
        else:
            raise ValueError()

        self._context = x[..., -context_size:]
        self._last_sr = sr
        self._last_batch_size = batch_size

        # out = torch.from_numpy(out)
        return out

    def audio_forward(self, audio: np.ndarray, sr: int):
        outs = []
        x, sr = self._validate_input(audio, sr)
        self.reset_states()
        num_samples = 512 if sr == 16000 else 256

        if x.shape[1] % num_samples:
            pad_num = num_samples - (x.shape[1] % num_samples)
            x = np.pad(x, ((0, 0), (0, pad_num)), 'constant', constant_values=(0.0, 0.0))

        for i in range(0, x.shape[1], num_samples):
            wavs_batch = x[:, i:i + num_samples]
            out_chunk = self.__call__(wavs_batch, sr)
            outs.append(out_chunk)

        stacked = np.concatenate(outs, axis=1)
        return stacked


class VADIteratorOnnx:
    def __init__(self,
                 threshold: float = 0.5,
                 sampling_rate: int = 16000,
                 min_silence_duration_ms: int = 100,
                 max_speech_duration_s: float = float('inf'),
                 speech_pad_ms: int = 30
                 ):
        self.model = OnnxWrapper(VAD_MODEL_PATH, True)
        self.threshold = threshold
        self.sampling_rate = sampling_rate

        if sampling_rate not in [8000, 16000]:
            raise ValueError('VADIterator does not support sampling rates other than [8000, 16000]')

        self.min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
        # self.max_speech_samples = int(sampling_rate * max_speech_duration_s)
        self.speech_pad_samples = sampling_rate * speech_pad_ms / 1000
        self.reset_states()

    def reset_states(self):

        self.model.reset_states()
        self.triggered = False
        self.temp_end = 0
        self.current_sample = 0
        self.start = 0

    def __call__(self, x: np.ndarray, return_seconds=False):
        """
        x: np.ndarray
            audio chunk (see examples in repo)

        return_seconds: bool (default - False)
            whether return timestamps in seconds (default - samples)
        """

        window_size_samples = 512 if self.sampling_rate == 16000 else 256
        x = x[:window_size_samples]
        if len(x) < window_size_samples:
            x = np.pad(x, ((0, 0), (0, window_size_samples - len(x))), 'constant', constant_values=0.0)

        self.current_sample += window_size_samples

        speech_prob = self.model(x, self.sampling_rate)[0,0]


        if (speech_prob >= self.threshold) and self.temp_end:
            self.temp_end = 0

        if (speech_prob >= self.threshold) and not self.triggered:
            self.triggered = True
            # speech_start = max(0, self.current_sample - window_size_samples)
            speech_start = max(0, self.current_sample - self.speech_pad_samples - window_size_samples)
            self.start = speech_start
            return {'start': int(speech_start) if not return_seconds else round(speech_start / self.sampling_rate, 1)}

        # if (speech_prob >= self.threshold) and self.current_sample - self.start >= self.max_speech_samples:
        #     if self.temp_end:
        #         self.temp_end = 0
        #     self.start = self.current_sample
        #     return {'end': int(self.current_sample) if not return_seconds else round(self.current_sample / self.sampling_rate, 1)}

        if (speech_prob < self.threshold - 0.15) and self.triggered:
            if not self.temp_end:
                self.temp_end = self.current_sample
            if self.current_sample - self.temp_end < self.min_silence_samples:
                return None
            else:
                # speech_end = self.temp_end - window_size_samples
                speech_end = self.temp_end + self.speech_pad_samples - window_size_samples
                self.temp_end = 0
                self.triggered = False
                return {'end': int(speech_end) if not return_seconds else round(speech_end / self.sampling_rate, 1)}

        return None




class FixedVADIterator(VADIteratorOnnx):
    '''It fixes VADIterator by allowing to process any audio length, not only exactly 512 frames at once.
    If audio to be processed at once is long and multiple voiced segments detected, 
    then __call__ returns the start of the first segment, and end (or middle, which means no end) of the last segment. 
    '''

    def reset_states(self):
        super().reset_states()
        self.buffer = np.array([],dtype=np.float32)

    def __call__(self, x, return_seconds=False):
        self.buffer = np.append(self.buffer, x) 
        ret = None
        while len(self.buffer) >= 512:
            r = super().__call__(self.buffer[:512], return_seconds=return_seconds)
            self.buffer = self.buffer[512:]
            if ret is None:
                ret = r
            elif r is not None:
                if 'end' in r:
                    ret['end'] = r['end']  # the latter end
                if 'start' in r and 'end' in ret:  # there is an earlier start.
                    # Remove end, merging this segment with the previous one.
                    del ret['end']
        return ret if ret != {} else None

class VadV2:
    def __init__(self,
                 threshold: float = 0.5,
                 sampling_rate: int = 16000,
                 min_silence_duration_ms: int = 100,
                 speech_pad_ms: int = 30,
                 max_speech_duration_s: float = float('inf')):
        # self.vad_iterator = VADIterator(threshold, sampling_rate, min_silence_duration_ms)
        self.vad_iterator = VADIteratorOnnx(threshold, sampling_rate, min_silence_duration_ms, max_speech_duration_s)
        self.speech_pad_samples = int(sampling_rate * speech_pad_ms / 1000)
        self.sampling_rate = sampling_rate
        self.audio_buffer = np.array([], dtype=np.float32)
        self.start = 0
        self.end = 0
        self.offset = 0
        assert speech_pad_ms <= min_silence_duration_ms, "speech_pad_ms should be less than min_silence_duration_ms"
        self.max_speech_samples = int(sampling_rate * max_speech_duration_s)

        self.silence_chunk_size = 0
        self.silence_chunk_threshold = 60 / (512 / self.sampling_rate)

    def reset(self):
        self.audio_buffer = np.array([], dtype=np.float32)
        self.start = 0
        self.end = 0
        self.offset = 0
        self.vad_iterator.reset_states()

    def __call__(self, x: np.ndarray = None):
        if x is None:
            if self.start:
                start = max(self.offset, self.start - self.speech_pad_samples)
                end = self.offset + len(self.audio_buffer)
                start_ts = round(start / self.sampling_rate, 1)
                end_ts = round(end / self.sampling_rate, 1)
                audio_data = self.audio_buffer[start - self.offset: end - self.offset]
                result = {
                    "start": start_ts,
                    "end": end_ts,
                    "audio": audio_data,
                }
            else:
                result = None
            self.reset()
            return result

        self.audio_buffer = np.append(self.audio_buffer, deepcopy(x))

        result = self.vad_iterator(x)
        if result is not None:
            # self.start = result.get('start', self.start)
            # self.end = result.get('end', self.end)
            self.silence_chunk_size = 0

            if 'start' in result:
                self.start = result['start']
            if 'end' in result:
                self.end = result['end']
        else:
            self.silence_chunk_size += 1

        if self.start == 0 and len(self.audio_buffer) > self.speech_pad_samples:
            self.offset += len(self.audio_buffer) - self.speech_pad_samples
            self.audio_buffer = self.audio_buffer[-self.speech_pad_samples:]

        if self.silence_chunk_size >= self.silence_chunk_threshold:
            self.offset += len(self.audio_buffer) - self.speech_pad_samples
            self.audio_buffer = self.audio_buffer[-self.speech_pad_samples:]
            self.silence_chunk_size = 0

        if self.end > self.start:
            start = max(self.offset, self.start - self.speech_pad_samples)
            end = self.end + self.speech_pad_samples
            start_ts = round(start / self.sampling_rate, 1)
            end_ts = round(end / self.sampling_rate, 1)
            audio_data = self.audio_buffer[start - self.offset: end - self.offset]
            self.audio_buffer = self.audio_buffer[self.end - self.offset:]
            self.offset = self.end
            self.start = self.end
            # self.start = 0
            self.end = 0
            result = {
                "start": start_ts,
                "end": end_ts,
                "audio": audio_data,
            }

            return result
        return None


class SileroVADProcessor:
    """
    A class for processing audio files using Silero VAD to detect voice activity
    and extract voice segments from audio files.
    """

    def __init__(self,
                 activate_threshold=0.5,
                 fusion_threshold=0.3,
                 min_speech_duration=0.25,
                 max_speech_duration=20,
                 min_silence_duration=250,
                 sample_rate=16000,
                 ort_providers=None):
        """
        Initialize the SileroVADProcessor.
        Args:
            activate_threshold (float): Threshold for voice activity detection
            fusion_threshold (float): Threshold for merging close speech segments (seconds)
            min_speech_duration (float): Minimum duration of speech to be considered valid (seconds)
            max_speech_duration (float): Maximum duration of speech (seconds)
            min_silence_duration (int): Minimum silence duration (ms)
            sample_rate (int): Sample rate of the audio (8000 or 16000 Hz)
            ort_providers (list): ONNX Runtime providers for acceleration
        """
        # VAD parameters
        self.activate_threshold = activate_threshold
        self.fusion_threshold = fusion_threshold
        self.min_speech_duration = min_speech_duration
        self.max_speech_duration = max_speech_duration
        self.min_silence_duration = min_silence_duration
        self.sample_rate = sample_rate
        self.ort_providers = ort_providers if ort_providers else []

        # Initialize logger
        self.logger = logging.getLogger(__name__)

        # Load Silero VAD model
        self._init_onnx_session()
        self.silero_vad = load_silero_vad(onnx=True)

    def _init_onnx_session(self):
        """Initialize ONNX Runtime session with appropriate settings."""
        session_opts = onnxruntime.SessionOptions()
        session_opts.log_severity_level = 3
        session_opts.inter_op_num_threads = 0
        session_opts.intra_op_num_threads = 0
        session_opts.enable_cpu_mem_arena = True
        session_opts.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
        session_opts.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL

        session_opts.add_session_config_entry("session.intra_op.allow_spinning", "1")
        session_opts.add_session_config_entry("session.inter_op.allow_spinning", "1")
        session_opts.add_session_config_entry("session.set_denormal_as_zero", "1")

        # Set the session_opts to be used by silero_vad
        # onnxruntime.capi._pybind_state.get_default_session_options(session_opts)

    def load_audio(self, audio_path):
        """
        Load audio file and prepare it for VAD processing.
        Args:
            audio_path (str): Path to the audio file
        Returns:
            numpy.ndarray: Audio data as numpy array
        """
        self.logger.info(f"Loading audio from {audio_path}")
        audio_segment = AudioSegment.from_file(audio_path)
        audio_segment = audio_segment.set_channels(1).set_frame_rate(self.sample_rate)

        # Convert to numpy array and normalize
        dtype = np.float16 if self.use_gpu_fp16 else np.float32
        audio_array = np.array(audio_segment.get_array_of_samples(), dtype=dtype) * 0.000030517578  # 1/32768

        self.audio_segment = audio_segment  # Store for later use
        return audio_array
    
    @property
    def model(self):
        return self.silero_vad

    def process_timestamps(self, timestamps):
        """
        Process VAD timestamps: filter short segments and merge close segments.
        Args:
            timestamps (list): List of (start, end) tuples
        Returns:
            list: Processed list of (start, end) tuples
        """
        # Filter out short durations
        filtered_timestamps = [(start, end) for start, end in timestamps
                               if (end - start) >= self.min_speech_duration]

        # Fuse timestamps in two passes for better merging
        fused_timestamps_1st = []
        for start, end in filtered_timestamps:
            if fused_timestamps_1st and (start - fused_timestamps_1st[-1][1] <= self.fusion_threshold):
                fused_timestamps_1st[-1] = (fused_timestamps_1st[-1][0], end)
            else:
                fused_timestamps_1st.append((start, end))

        fused_timestamps_2nd = []
        for start, end in fused_timestamps_1st:
            if fused_timestamps_2nd and (start - fused_timestamps_2nd[-1][1] <= self.fusion_threshold):
                fused_timestamps_2nd[-1] = (fused_timestamps_2nd[-1][0], end)
            else:
                fused_timestamps_2nd.append((start, end))

        return fused_timestamps_2nd

    def format_time(self, seconds):
        """
        Convert seconds to VTT time format 'hh:mm:ss.mmm'.
        Args:
            seconds (float): Time in seconds
        Returns:
            str: Formatted time string
        """
        td = timedelta(seconds=seconds)
        td_sec = td.total_seconds()
        total_seconds = int(td_sec)
        milliseconds = int((td_sec - total_seconds) * 1000)
        hours = total_seconds // 3600
        minutes = (total_seconds % 3600) // 60
        seconds = total_seconds % 60
        return f"{hours:02}:{minutes:02}:{seconds:02}.{milliseconds:03}"

    def detect_speech(self, audio:np.array):
        """
        Run VAD on the audio file to detect speech segments.
        Args:
            audio_path (str): Path to the audio file
        Returns:
            list: List of processed timestamps as (start, end) tuples
        """
        self.logger.info("Starting VAD process")
        start_time = time.time()
        # Get speech timestamps
        raw_timestamps = get_speech_timestamps(
            audio,
            model=self.silero_vad,
            threshold=self.activate_threshold,
            max_speech_duration_s=self.max_speech_duration,
            min_speech_duration_ms=int(self.min_speech_duration * 1000),
            min_silence_duration_ms=self.min_silence_duration,
            return_seconds=True
        )

        # Convert to simple format and process
        timestamps = [(item['start'], item['end']) for item in raw_timestamps]
        processed_timestamps = self.process_timestamps(timestamps)

        # Clean up
        del audio
        gc.collect()

        self.logger.info(f"VAD completed in {time.time() - start_time:.3f} seconds")
        return processed_timestamps

        """
        Save timestamps in both second and sample indices formats.
        Args:
            timestamps (list): List of (start, end) tuples
            output_prefix (str): Prefix for output files
        """
        # Save timestamps in seconds (VTT format)
        seconds_path = f"{output_prefix}_timestamps_second.txt"
        with open(seconds_path, "w", encoding='UTF-8') as file:
            self.logger.info("Saving timestamps in seconds format")
            for start, end in timestamps:
                s_time = self.format_time(start)
                e_time = self.format_time(end)
                line = f"{s_time} --> {e_time}\n"
                file.write(line)

        # Save timestamps in sample indices
        indices_path = f"{output_prefix}_timestamps_indices.txt"
        with open(indices_path, "w", encoding='UTF-8') as file:
            self.logger.info("Saving timestamps in indices format")
            for start, end in timestamps:
                line = f"{int(start * self.sample_rate)} --> {int(end * self.sample_rate)}\n"
                file.write(line)

        self.logger.info(f"Timestamps saved to {seconds_path} and {indices_path}")

    def extract_speech_segments(self, audio_segment, timestamps):
        """
        Extract speech segments from the audio and combine them into a single audio file.
        Args:
            timestamps (list): List of (start, end) tuples indicating speech segments
        Returns:
            AudioSegment: The combined speech segments
        """
        audio_segment = audio_segment.numpy()
        combined_speech = np.array([], dtype=np.float32)

        # Extract and combine each speech segment
        for i, (start, end) in enumerate(timestamps):
            # Convert seconds to milliseconds for pydub
            start_ms = int(start * 1000)
            end_ms = int(end * 1000)

            # Ensure the end time does not exceed the length of the audio segment
            if end_ms > len(audio_segment):
                end_ms = len(audio_segment)

            # Extract the segment
            segment = audio_segment[start_ms:end_ms]

            # Add to combined audio
            combined_speech = np.append(combined_speech, segment)

        return combined_speech

    def process_audio(self, audio_array:np.array):
        """
        Complete processing pipeline: detect speech, save timestamps, and optionally extract speech.
        Returns:
            tuple: (timestamps, output_speech_path if extract_speech else None)
        """

        # Run VAD to detect speech
        timestamps = self.detect_speech(audio_array)

        combined_speech = self.extract_speech_segments(audio_array, timestamps)

        return timestamps, combined_speech



class VadProcessor:
    def __init__(
            self,
            prob_threshold=0.5,
            silence_s=0.2,
            cache_s=0.15,
            sr=16000
    ):
        self.prob_threshold = prob_threshold
        self.cache_s = cache_s
        self.sr = sr
        self.silence_s = silence_s

        self.vad = VadV2(self.prob_threshold, self.sr, self.silence_s * 1000, self.cache_s * 1000, max_speech_duration_s=15)


    def process_audio(self, audio_buffer: np.ndarray):
        audio = np.array([], np.float32)
        for i in range(0, len(audio_buffer), 512):
            chunk = audio_buffer[i:i+512]
            ret = self.vad(chunk)
            if ret:
                audio = np.append(audio, ret['audio'])
        return audio