diff options
Diffstat (limited to 'third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/quality_assessment/annotations.py')
| -rw-r--r-- | third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/quality_assessment/annotations.py | 296 |
1 files changed, 296 insertions, 0 deletions
diff --git a/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/quality_assessment/annotations.py b/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/quality_assessment/annotations.py new file mode 100644 index 0000000000..93a8248397 --- /dev/null +++ b/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/quality_assessment/annotations.py @@ -0,0 +1,296 @@ +# Copyright (c) 2017 The WebRTC project authors. All Rights Reserved. +# +# Use of this source code is governed by a BSD-style license +# that can be found in the LICENSE file in the root of the source +# tree. An additional intellectual property rights grant can be found +# in the file PATENTS. All contributing project authors may +# be found in the AUTHORS file in the root of the source tree. +"""Extraction of annotations from audio files. +""" + +from __future__ import division +import logging +import os +import shutil +import struct +import subprocess +import sys +import tempfile + +try: + import numpy as np +except ImportError: + logging.critical('Cannot import the third-party Python package numpy') + sys.exit(1) + +from . import external_vad +from . import exceptions +from . import signal_processing + + +class AudioAnnotationsExtractor(object): + """Extracts annotations from audio files. + """ + + class VadType(object): + ENERGY_THRESHOLD = 1 # TODO(alessiob): Consider switching to P56 standard. + WEBRTC_COMMON_AUDIO = 2 # common_audio/vad/include/vad.h + WEBRTC_APM = 4 # modules/audio_processing/vad/vad.h + + def __init__(self, value): + if (not isinstance(value, int)) or not 0 <= value <= 7: + raise exceptions.InitializationException('Invalid vad type: ' + + value) + self._value = value + + def Contains(self, vad_type): + return self._value | vad_type == self._value + + def __str__(self): + vads = [] + if self.Contains(self.ENERGY_THRESHOLD): + vads.append("energy") + if self.Contains(self.WEBRTC_COMMON_AUDIO): + vads.append("common_audio") + if self.Contains(self.WEBRTC_APM): + vads.append("apm") + return "VadType({})".format(", ".join(vads)) + + _OUTPUT_FILENAME_TEMPLATE = '{}annotations.npz' + + # Level estimation params. + _ONE_DB_REDUCTION = np.power(10.0, -1.0 / 20.0) + _LEVEL_FRAME_SIZE_MS = 1.0 + # The time constants in ms indicate the time it takes for the level estimate + # to go down/up by 1 db if the signal is zero. + _LEVEL_ATTACK_MS = 5.0 + _LEVEL_DECAY_MS = 20.0 + + # VAD params. + _VAD_THRESHOLD = 1 + _VAD_WEBRTC_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), + os.pardir, os.pardir) + _VAD_WEBRTC_COMMON_AUDIO_PATH = os.path.join(_VAD_WEBRTC_PATH, 'vad') + + _VAD_WEBRTC_APM_PATH = os.path.join(_VAD_WEBRTC_PATH, 'apm_vad') + + def __init__(self, vad_type, external_vads=None): + self._signal = None + self._level = None + self._level_frame_size = None + self._common_audio_vad = None + self._energy_vad = None + self._apm_vad_probs = None + self._apm_vad_rms = None + self._vad_frame_size = None + self._vad_frame_size_ms = None + self._c_attack = None + self._c_decay = None + + self._vad_type = self.VadType(vad_type) + logging.info('VADs used for annotations: ' + str(self._vad_type)) + + if external_vads is None: + external_vads = {} + self._external_vads = external_vads + + assert len(self._external_vads) == len(external_vads), ( + 'The external VAD names must be unique.') + for vad in external_vads.values(): + if not isinstance(vad, external_vad.ExternalVad): + raise exceptions.InitializationException('Invalid vad type: ' + + str(type(vad))) + logging.info('External VAD used for annotation: ' + str(vad.name)) + + assert os.path.exists(self._VAD_WEBRTC_COMMON_AUDIO_PATH), \ + self._VAD_WEBRTC_COMMON_AUDIO_PATH + assert os.path.exists(self._VAD_WEBRTC_APM_PATH), \ + self._VAD_WEBRTC_APM_PATH + + @classmethod + def GetOutputFileNameTemplate(cls): + return cls._OUTPUT_FILENAME_TEMPLATE + + def GetLevel(self): + return self._level + + def GetLevelFrameSize(self): + return self._level_frame_size + + @classmethod + def GetLevelFrameSizeMs(cls): + return cls._LEVEL_FRAME_SIZE_MS + + def GetVadOutput(self, vad_type): + if vad_type == self.VadType.ENERGY_THRESHOLD: + return self._energy_vad + elif vad_type == self.VadType.WEBRTC_COMMON_AUDIO: + return self._common_audio_vad + elif vad_type == self.VadType.WEBRTC_APM: + return (self._apm_vad_probs, self._apm_vad_rms) + else: + raise exceptions.InitializationException('Invalid vad type: ' + + vad_type) + + def GetVadFrameSize(self): + return self._vad_frame_size + + def GetVadFrameSizeMs(self): + return self._vad_frame_size_ms + + def Extract(self, filepath): + # Load signal. + self._signal = signal_processing.SignalProcessingUtils.LoadWav( + filepath) + if self._signal.channels != 1: + raise NotImplementedError( + 'Multiple-channel annotations not implemented') + + # Level estimation params. + self._level_frame_size = int(self._signal.frame_rate / 1000 * + (self._LEVEL_FRAME_SIZE_MS)) + self._c_attack = 0.0 if self._LEVEL_ATTACK_MS == 0 else ( + self._ONE_DB_REDUCTION**(self._LEVEL_FRAME_SIZE_MS / + self._LEVEL_ATTACK_MS)) + self._c_decay = 0.0 if self._LEVEL_DECAY_MS == 0 else ( + self._ONE_DB_REDUCTION**(self._LEVEL_FRAME_SIZE_MS / + self._LEVEL_DECAY_MS)) + + # Compute level. + self._LevelEstimation() + + # Ideal VAD output, it requires clean speech with high SNR as input. + if self._vad_type.Contains(self.VadType.ENERGY_THRESHOLD): + # Naive VAD based on level thresholding. + vad_threshold = np.percentile(self._level, self._VAD_THRESHOLD) + self._energy_vad = np.uint8(self._level > vad_threshold) + self._vad_frame_size = self._level_frame_size + self._vad_frame_size_ms = self._LEVEL_FRAME_SIZE_MS + if self._vad_type.Contains(self.VadType.WEBRTC_COMMON_AUDIO): + # WebRTC common_audio/ VAD. + self._RunWebRtcCommonAudioVad(filepath, self._signal.frame_rate) + if self._vad_type.Contains(self.VadType.WEBRTC_APM): + # WebRTC modules/audio_processing/ VAD. + self._RunWebRtcApmVad(filepath) + for extvad_name in self._external_vads: + self._external_vads[extvad_name].Run(filepath) + + def Save(self, output_path, annotation_name=""): + ext_kwargs = { + 'extvad_conf-' + ext_vad: + self._external_vads[ext_vad].GetVadOutput() + for ext_vad in self._external_vads + } + np.savez_compressed(file=os.path.join( + output_path, + self.GetOutputFileNameTemplate().format(annotation_name)), + level=self._level, + level_frame_size=self._level_frame_size, + level_frame_size_ms=self._LEVEL_FRAME_SIZE_MS, + vad_output=self._common_audio_vad, + vad_energy_output=self._energy_vad, + vad_frame_size=self._vad_frame_size, + vad_frame_size_ms=self._vad_frame_size_ms, + vad_probs=self._apm_vad_probs, + vad_rms=self._apm_vad_rms, + **ext_kwargs) + + def _LevelEstimation(self): + # Read samples. + samples = signal_processing.SignalProcessingUtils.AudioSegmentToRawData( + self._signal).astype(np.float32) / 32768.0 + num_frames = len(samples) // self._level_frame_size + num_samples = num_frames * self._level_frame_size + + # Envelope. + self._level = np.max(np.reshape(np.abs(samples[:num_samples]), + (num_frames, self._level_frame_size)), + axis=1) + assert len(self._level) == num_frames + + # Envelope smoothing. + smooth = lambda curr, prev, k: (1 - k) * curr + k * prev + self._level[0] = smooth(self._level[0], 0.0, self._c_attack) + for i in range(1, num_frames): + self._level[i] = smooth( + self._level[i], self._level[i - 1], self._c_attack if + (self._level[i] > self._level[i - 1]) else self._c_decay) + + def _RunWebRtcCommonAudioVad(self, wav_file_path, sample_rate): + self._common_audio_vad = None + self._vad_frame_size = None + + # Create temporary output path. + tmp_path = tempfile.mkdtemp() + output_file_path = os.path.join( + tmp_path, + os.path.split(wav_file_path)[1] + '_vad.tmp') + + # Call WebRTC VAD. + try: + subprocess.call([ + self._VAD_WEBRTC_COMMON_AUDIO_PATH, '-i', wav_file_path, '-o', + output_file_path + ], + cwd=self._VAD_WEBRTC_PATH) + + # Read bytes. + with open(output_file_path, 'rb') as f: + raw_data = f.read() + + # Parse side information. + self._vad_frame_size_ms = struct.unpack('B', raw_data[0])[0] + self._vad_frame_size = self._vad_frame_size_ms * sample_rate / 1000 + assert self._vad_frame_size_ms in [10, 20, 30] + extra_bits = struct.unpack('B', raw_data[-1])[0] + assert 0 <= extra_bits <= 8 + + # Init VAD vector. + num_bytes = len(raw_data) + num_frames = 8 * (num_bytes - + 2) - extra_bits # 8 frames for each byte. + self._common_audio_vad = np.zeros(num_frames, np.uint8) + + # Read VAD decisions. + for i, byte in enumerate(raw_data[1:-1]): + byte = struct.unpack('B', byte)[0] + for j in range(8 if i < num_bytes - 3 else (8 - extra_bits)): + self._common_audio_vad[i * 8 + j] = int(byte & 1) + byte = byte >> 1 + except Exception as e: + logging.error('Error while running the WebRTC VAD (' + e.message + + ')') + finally: + if os.path.exists(tmp_path): + shutil.rmtree(tmp_path) + + def _RunWebRtcApmVad(self, wav_file_path): + # Create temporary output path. + tmp_path = tempfile.mkdtemp() + output_file_path_probs = os.path.join( + tmp_path, + os.path.split(wav_file_path)[1] + '_vad_probs.tmp') + output_file_path_rms = os.path.join( + tmp_path, + os.path.split(wav_file_path)[1] + '_vad_rms.tmp') + + # Call WebRTC VAD. + try: + subprocess.call([ + self._VAD_WEBRTC_APM_PATH, '-i', wav_file_path, '-o_probs', + output_file_path_probs, '-o_rms', output_file_path_rms + ], + cwd=self._VAD_WEBRTC_PATH) + + # Parse annotations. + self._apm_vad_probs = np.fromfile(output_file_path_probs, + np.double) + self._apm_vad_rms = np.fromfile(output_file_path_rms, np.double) + assert len(self._apm_vad_rms) == len(self._apm_vad_probs) + + except Exception as e: + logging.error('Error while running the WebRTC APM VAD (' + + e.message + ')') + finally: + if os.path.exists(tmp_path): + shutil.rmtree(tmp_path) |