Rename to Linux Voice Assistant
This commit is contained in:
Michael Hansen
@@ -0,0 +1,272 @@
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import ctypes
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import json
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import statistics
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from collections import deque
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from pathlib import Path
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from typing import Deque, List, Union
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import numpy as np
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from pymicro_features import MicroFrontend
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_SAMPLES_PER_SECOND = 16000
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_SAMPLES_PER_CHUNK = 160 # 10ms
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_BYTES_PER_SAMPLE = 2 # 16-bit
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_BYTES_PER_CHUNK = _SAMPLES_PER_CHUNK * _BYTES_PER_SAMPLE
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_SECONDS_PER_CHUNK = _SAMPLES_PER_CHUNK / _SAMPLES_PER_SECOND
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_STRIDE = 3
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_DEFAULT_REFRACTORY = 2 # seconds
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class TfLiteQuantizationParams(ctypes.Structure):
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_fields_ = [("scale", ctypes.c_float), ("zero_point", ctypes.c_int32)]
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class MicroWakeWord:
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def __init__(
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self,
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id: str, # pylint: disable=redefined-builtin
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wake_word: str,
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tflite_model: Union[str, Path],
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probability_cutoff: float,
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sliding_window_size: int,
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refractory_seconds: float,
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trained_languages: List[str],
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libtensorflowlite_c_path: Union[str, Path],
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):
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self.id = id
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self.wake_word = wake_word
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self.tflite_model = tflite_model
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self.probability_cutoff = probability_cutoff
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self.sliding_window_size = sliding_window_size
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self.refractory_seconds = refractory_seconds
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self.trained_languages = trained_languages
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self.is_active = True
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# Load the shared library
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self.libtensorflowlite_c_path = Path(libtensorflowlite_c_path)
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self.lib = ctypes.cdll.LoadLibrary(str(self.libtensorflowlite_c_path.resolve()))
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# Define required argument/return types for C API
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self.lib.TfLiteModelCreateFromFile.argtypes = [ctypes.c_char_p]
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self.lib.TfLiteModelCreateFromFile.restype = ctypes.c_void_p
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self.lib.TfLiteInterpreterCreate.argtypes = [ctypes.c_void_p, ctypes.c_void_p]
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self.lib.TfLiteInterpreterCreate.restype = ctypes.c_void_p
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self.lib.TfLiteInterpreterAllocateTensors.argtypes = [ctypes.c_void_p]
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self.lib.TfLiteInterpreterInvoke.argtypes = [ctypes.c_void_p]
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self.lib.TfLiteInterpreterGetInputTensor.argtypes = [
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ctypes.c_void_p,
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ctypes.c_int,
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]
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self.lib.TfLiteInterpreterGetInputTensor.restype = ctypes.c_void_p
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self.lib.TfLiteInterpreterGetOutputTensor.argtypes = [
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ctypes.c_void_p,
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ctypes.c_int,
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]
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self.lib.TfLiteInterpreterGetOutputTensor.restype = ctypes.c_void_p
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self.lib.TfLiteTensorCopyFromBuffer.argtypes = [
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ctypes.c_void_p,
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ctypes.c_void_p,
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ctypes.c_size_t,
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]
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self.lib.TfLiteTensorCopyToBuffer.argtypes = [
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ctypes.c_void_p,
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ctypes.c_void_p,
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ctypes.c_size_t,
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]
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self.lib.TfLiteTensorType.restype = ctypes.c_int
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self.lib.TfLiteTensorByteSize.argtypes = [ctypes.c_void_p]
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self.lib.TfLiteTensorByteSize.restype = ctypes.c_size_t
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self.lib.TfLiteTensorQuantizationParams.restype = ctypes.Structure
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self.lib.TfLiteTensorQuantizationParams.argtypes = [ctypes.c_void_p]
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self.lib.TfLiteTensorQuantizationParams.restype = TfLiteQuantizationParams
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# Load the model and create interpreter
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self.model_path = str(Path(tflite_model).resolve()).encode("utf-8")
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self._load_model()
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self._frontend = MicroFrontend()
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self._features: List[np.ndarray] = []
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self._probabilities: Deque[float] = deque(maxlen=self.sliding_window_size)
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self._audio_buffer = bytes()
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self._ignore_seconds: float = 0
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def _load_model(self) -> None:
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self.model = self.lib.TfLiteModelCreateFromFile(self.model_path)
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self.interpreter = self.lib.TfLiteInterpreterCreate(self.model, None)
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self.lib.TfLiteInterpreterAllocateTensors(self.interpreter)
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# Access input and output tensor
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self.input_tensor = self.lib.TfLiteInterpreterGetInputTensor(
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self.interpreter, 0
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)
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self.output_tensor = self.lib.TfLiteInterpreterGetOutputTensor(
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self.interpreter, 0
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)
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# Get quantization parameters
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input_q = self.lib.TfLiteTensorQuantizationParams(self.input_tensor)
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output_q = self.lib.TfLiteTensorQuantizationParams(self.output_tensor)
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self.input_scale, self.input_zero_point = input_q.scale, input_q.zero_point
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self.output_scale, self.output_zero_point = output_q.scale, output_q.zero_point
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def reset(self) -> None:
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"""Reload model and clear state.
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This must be done between audio clips when not streaming.
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"""
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self._audio_buffer = bytes()
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self._features.clear()
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self._probabilities.clear()
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self._ignore_seconds = 0
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# Clear out residual features
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self._frontend = MicroFrontend()
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# Need to reload model to reset intermediary results
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# reset_all_variables() doesn't work.
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self._load_model()
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@property
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def samples_per_chunk(self) -> int:
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"""Number of samples in a streaming audio chunk."""
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return _SAMPLES_PER_CHUNK
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@property
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def bytes_per_chunk(self) -> int:
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"""Number of bytes in a streaming audio chunk.
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Assumes 16-bit mono samples at 16Khz.
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"""
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return _BYTES_PER_CHUNK
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@staticmethod
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def from_config(
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config_path: Union[str, Path],
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libtensorflowlite_c_path: Union[str, Path],
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refractory_seconds: float = _DEFAULT_REFRACTORY,
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) -> "MicroWakeWord":
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"""Load a microWakeWord model from a JSON config file.
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Parameters
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----------
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config_path: str or Path
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Path to JSON configuration file
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refractory_seconds: float
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Number of seconds to ignore after detection
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"""
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config_path = Path(config_path)
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with open(config_path, "r", encoding="utf-8") as config_file:
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config = json.load(config_file)
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micro_config = config["micro"]
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return MicroWakeWord(
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id=Path(config["model"]).stem,
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wake_word=config["wake_word"],
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tflite_model=config_path.parent / config["model"],
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probability_cutoff=micro_config["probability_cutoff"],
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sliding_window_size=micro_config["sliding_window_size"],
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refractory_seconds=refractory_seconds,
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trained_languages=micro_config.get("trained_languages", []),
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libtensorflowlite_c_path=libtensorflowlite_c_path,
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)
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def process_streaming(self, audio_bytes: bytes) -> bool:
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"""Process a chunk of audio in streaming mode.
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Parameters
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----------
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audio_bytes: bytes
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Raw 16-bit mono audio samples at 16Khz
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Returns True if the wake word was detected.
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"""
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self._audio_buffer += audio_bytes
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if len(self._audio_buffer) < _BYTES_PER_CHUNK:
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# Not enough audio to get features
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return False
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detected = False
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audio_buffer_idx = 0
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while (audio_buffer_idx + _BYTES_PER_CHUNK) <= len(self._audio_buffer):
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# Process chunk
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chunk_bytes = self._audio_buffer[
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audio_buffer_idx : audio_buffer_idx + _BYTES_PER_CHUNK
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]
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frontend_result = self._frontend.ProcessSamples(chunk_bytes)
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audio_buffer_idx += frontend_result.samples_read * _BYTES_PER_SAMPLE
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self._ignore_seconds = max(0, self._ignore_seconds - _SECONDS_PER_CHUNK)
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if not frontend_result.features:
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# Not enough audio for a full window
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continue
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self._features.append(
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np.array(frontend_result.features).reshape(
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(1, 1, len(frontend_result.features))
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)
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)
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if len(self._features) < _STRIDE:
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# Not enough windows
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continue
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# Allocate and quantize input data
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quant_features = np.round(
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np.concatenate(self._features, axis=1) / self.input_scale
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+ self.input_zero_point
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).astype(np.uint8)
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# Stride instead of rolling
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self._features.clear()
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# Set tensor
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quant_ptr = quant_features.ctypes.data_as(ctypes.c_void_p)
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self.lib.TfLiteTensorCopyFromBuffer(
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self.input_tensor, quant_ptr, quant_features.nbytes
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)
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# Run inference
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self.lib.TfLiteInterpreterInvoke(self.interpreter)
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# Read output
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output_bytes = self.lib.TfLiteTensorByteSize(self.output_tensor)
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output_data = np.empty(output_bytes, dtype=np.uint8)
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self.lib.TfLiteTensorCopyToBuffer(
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self.output_tensor,
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output_data.ctypes.data_as(ctypes.c_void_p),
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output_bytes,
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)
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# Dequantize output
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result = (
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output_data.astype(np.float32) - self.output_zero_point
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) * self.output_scale
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self._probabilities.append(result.item())
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if len(self._probabilities) < self.sliding_window_size:
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# Not enough probabilities
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continue
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if statistics.mean(self._probabilities) > self.probability_cutoff:
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if self._ignore_seconds <= 0:
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detected = True
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self._ignore_seconds = self.refractory_seconds
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# Remove processed audio
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self._audio_buffer = self._audio_buffer[audio_buffer_idx:]
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return detected
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