Delete modules/vad

modules/vad/silero_vad.py

@@ -1,295 +0,0 @@
-# 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 bisect
-import faster_whisper
-from faster_whisper.transcribe import SpeechTimestampsMap
-import gradio as gr
-
-class Segment:
-    def __init__(self):
-        self.id: Optional[int] = Field(default=None, description="Incremental id for the segment")
-        self.seek: Optional[int] = Field(default=None, description="Seek of the segment from chunked audio")
-        self.text: Optional[str] = Field(default=None, description="Transcription text of the segment")
-        self.start: Optional[float] = Field(default=None, description="Start time of the segment")
-        self.end: Optional[float] = Field(default=None, description="End time of the segment")
-        self.tokens: Optional[List[int]] = Field(default=None, description="List of token IDs")
-        self.temperature: Optional[float] = Field(default=None, description="Temperature used during the decoding process")
-        self.avg_logprob: Optional[float] = Field(default=None, description="Average log probability of the tokens")
-        self.compression_ratio: Optional[float] = Field(default=None, description="Compression ratio of the segment")
-        self.no_speech_prob: Optional[float] = Field(default=None, description="Probability that it's not speech")
-        self.words: Optional[List['Word']] = Field(default=None, description="List of words contained in the segment")
-
-class Word:
-    def __init__(self):
-        self.start: Optional[float] = Field(default=None, description="Start time of the word")
-        self.end: Optional[float] = Field(default=None, description="Start time of the word")
-        self.word: Optional[str] = Field(default=None, description="Word text")
-        self.probability: Optional[float] = Field(default=None, description="Probability of the word")
-
-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
-        neg_threshold = vad_options.neg_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
-        min_speech_samples = self.sampling_rate * min_speech_duration_ms / 1000
-        speech_pad_samples = self.sampling_rate * speech_pad_ms / 1000
-        max_speech_samples = (
-                self.sampling_rate * max_speech_duration_s
-                - window_size_samples
-                - 2 * speech_pad_samples
-        )
-        min_silence_samples = self.sampling_rate * min_silence_duration_ms / 1000
-        min_silence_samples_at_max_speech = self.sampling_rate * 98 / 1000
-
-        audio_length_samples = len(audio)
-
-        padded_audio = np.pad(
-            audio, (0, window_size_samples - audio.shape[0] % window_size_samples)
-        )
-        speech_probs = self.model(padded_audio.reshape(1, -1)).squeeze(0)
-
-        triggered = False
-        speeches = []
-        current_speech = {}
-        if neg_threshold is None:
-            neg_threshold = max(threshold - 0.15, 0.01)
-
-        # 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[Segment],
-        speech_chunks: List[dict],
-        sampling_rate: Optional[int] = None,
-    ) -> List[Segment]:
-        if sampling_rate is None:
-            sampling_rate = self.sampling_rate
-
-        ts_map = SpeechTimestampsMap(speech_chunks, sampling_rate)
-
-        for segment in segments:
-            if segment.words:
-                words = []
-                for word in segment.words:
-                    # Ensure the word start and end times are resolved to the same chunk.
-                    middle = (word.start + word.end) / 2
-                    chunk_index = ts_map.get_chunk_index(middle)
-                    word.start = ts_map.get_original_time(word.start, chunk_index)
-                    word.end = ts_map.get_original_time(word.end, chunk_index)
-                    words.append(word)
-
-                segment.start = words[0].start
-                segment.end = words[-1].end
-                segment.words = words
-
-            else:
-                segment.start = ts_map.get_original_time(segment.start)
-                segment.end = ts_map.get_original_time(segment.end)
-
-        return segments
-

modules/vad/silero_vad_backup.py

@@ -1,264 +0,0 @@
-# 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
-