From a62cb52f5fbc0cf701e1443d8abf415fc312b12c Mon Sep 17 00:00:00 2001 From: Yuta Hayashibe Date: Sat, 1 Oct 2022 23:21:58 +0900 Subject: [PATCH] Add --- poetry.lock | 34 +++++++++++++++++++++++++- pyproject.toml | 1 + whispering/schema.py | 7 ++++++ whispering/transcriber.py | 5 ++++ whispering/vad.py | 50 +++++++++++++++++++++++++++++++++++++++ 5 files changed, 96 insertions(+), 1 deletion(-) create mode 100644 whispering/vad.py diff --git a/poetry.lock b/poetry.lock index 4f43d3a..00bc0e5 100644 --- a/poetry.lock +++ b/poetry.lock @@ -378,6 +378,17 @@ python-versions = ">=3.7.0" [package.dependencies] typing-extensions = "*" +[[package]] +name = "torchaudio" +version = "0.12.1" +description = "An audio package for PyTorch" +category = "main" +optional = false +python-versions = "*" + +[package.dependencies] +torch = "1.12.1" + [[package]] name = "tqdm" version = "4.64.1" @@ -514,7 +525,7 @@ resolved_reference = "62fe7f1009a534986ac1d32a4aef8c244d029c28" [metadata] lock-version = "1.1" python-versions = ">=3.8,<3.11" -content-hash = "d041d21a202339f405cc37076403f92135ee1f113cdfece5a78c9ee12374be7b" +content-hash = "75e53434d1d46d54a886ca7a896a2f0ba0072a1848f90d5b6dc46ea2c5b47191" [metadata.files] black = [ @@ -964,6 +975,27 @@ torch = [ {file = "torch-1.12.1-cp39-none-macosx_10_9_x86_64.whl", hash = "sha256:bfec2843daa654f04fda23ba823af03e7b6f7650a873cdb726752d0e3718dada"}, {file = "torch-1.12.1-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:69fe2cae7c39ccadd65a123793d30e0db881f1c1927945519c5c17323131437e"}, ] +torchaudio = [ + {file = "torchaudio-0.12.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:dc138bee06b2305442fc132171f2a01d5f42509eaa21bdf87c3d26a6f4a09fdd"}, + {file = "torchaudio-0.12.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:1d81f71837d5d5be651e85ca9fa9377ecb4513b0129ddfb025540e1c2406d3e6"}, + {file = "torchaudio-0.12.1-cp310-cp310-manylinux1_x86_64.whl", hash = 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[tool.poetry.group.dev.dependencies] diff --git a/whispering/schema.py b/whispering/schema.py index 7ebecd4..6611347 100644 --- a/whispering/schema.py +++ b/whispering/schema.py @@ -2,6 +2,7 @@ from typing import List, Optional +import numpy as np import torch from pydantic import BaseModel, root_validator @@ -50,3 +51,9 @@ class ParsedChunk(BaseModel): avg_logprob: float compression_ratio: float no_speech_prob: float + + +class SpeechSegment(BaseModel, arbitrary_types_allowed=True): + start_block_idx: int + end_block_idx: int + segment: np.ndarray diff --git a/whispering/transcriber.py b/whispering/transcriber.py index 059d3c4..585842f 100644 --- a/whispering/transcriber.py +++ b/whispering/transcriber.py @@ -18,6 +18,7 @@ from whisper.tokenizer import get_tokenizer from whisper.utils import exact_div from whispering.schema import Context, ParsedChunk, WhisperConfig +from whispering.vad import VAD logger = getLogger(__name__) @@ -51,6 +52,7 @@ class WhisperStreamingTranscriber: self.time_precision: Final[float] = ( self.input_stride * HOP_LENGTH / SAMPLE_RATE ) # time per output token: 0.02 (seconds) + self.vad = VAD() def _get_decoding_options( self, @@ -233,6 +235,9 @@ class WhisperStreamingTranscriber: segment: np.ndarray, ctx: Context, ) -> Iterator[ParsedChunk]: + vad_probs = self.vad(segment) + logger.debug(f"{vad_probs}") + new_mel = log_mel_spectrogram(audio=segment).unsqueeze(0) logger.debug(f"Incoming new_mel.shape: {new_mel.shape}") if ctx.buffer_mel is None: diff --git a/whispering/vad.py b/whispering/vad.py new file mode 100644 index 0000000..c9218a9 --- /dev/null +++ b/whispering/vad.py @@ -0,0 +1,50 @@ +#!/usr/bin/env python3 + +from typing import Iterator + +import numpy as np +import torch +from whisper.audio import N_FRAMES, SAMPLE_RATE + +from whispering.schema import SpeechSegment + + +class VAD: + def __init__( + self, + ): + self.vad_model, _ = torch.hub.load( + repo_or_dir="snakers4/silero-vad", + model="silero_vad", + ) + + def __call__( + self, + *, + segment: np.ndarray, + thredhold: float = 0.5, + ) -> Iterator[SpeechBlock]: + # segment.shape should be multiple of (N_FRAMES,) + + block_size: int = int(segment.shape[0] / N_FRAMES) + + start_block_idx = None + for idx in range(block_size + 1): + if idx < block_size: + start: int = N_FRAMES * idx + end: int = N_FRAMES * (idx + 1) + vad_prob = self.vad_model( + torch.from_numpy(segment[start:end]), + SAMPLE_RATE, + ).item() + if vad_prob > thredhold: + if start_block_idx is None: + start_block_idx = idx + else: + if start_block_idx is not None: + yield SpeechSegment( + start_block_idx=start_block_idx, + end_block_idx=idx, + segment=segment[N_FRAMES * start_block_idx : N_FRAMES * idx], + ) + start_block_idx = None