mirror of https://github.com/coqui-ai/TTS.git
Create a module for the VAD script
Edresson
Eren Gölge
parent
390096fe0f
commit
89019d49a2
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@ -1,162 +1,31 @@
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# This code is adpated from: https://github.com/wiseman/py-webrtcvad/blob/master/example.py
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import argparse
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import collections
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import contextlib
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import glob
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import multiprocessing
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import os
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import glob
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import pathlib
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import wave
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import webrtcvad
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import argparse
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import multiprocessing
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from tqdm.contrib.concurrent import process_map
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def read_wave(path):
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"""Reads a .wav file.
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Takes the path, and returns (PCM audio data, sample rate).
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"""
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with contextlib.closing(wave.open(path, "rb")) as wf:
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num_channels = wf.getnchannels()
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assert num_channels == 1
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sample_width = wf.getsampwidth()
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assert sample_width == 2
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sample_rate = wf.getframerate()
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assert sample_rate in (8000, 16000, 32000, 48000)
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pcm_data = wf.readframes(wf.getnframes())
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return pcm_data, sample_rate
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def write_wave(path, audio, sample_rate):
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"""Writes a .wav file.
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Takes path, PCM audio data, and sample rate.
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"""
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with contextlib.closing(wave.open(path, "wb")) as wf:
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wf.setnchannels(1)
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wf.setsampwidth(2)
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wf.setframerate(sample_rate)
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wf.writeframes(audio)
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class Frame(object):
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"""Represents a "frame" of audio data."""
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def __init__(self, _bytes, timestamp, duration):
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self.bytes =_bytes
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self.timestamp = timestamp
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self.duration = duration
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def frame_generator(frame_duration_ms, audio, sample_rate):
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"""Generates audio frames from PCM audio data.
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Takes the desired frame duration in milliseconds, the PCM data, and
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the sample rate.
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Yields Frames of the requested duration.
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"""
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n = int(sample_rate * (frame_duration_ms / 1000.0) * 2)
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offset = 0
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timestamp = 0.0
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duration = (float(n) / sample_rate) / 2.0
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while offset + n < len(audio):
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yield Frame(audio[offset : offset + n], timestamp, duration)
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timestamp += duration
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offset += n
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def vad_collector(sample_rate, frame_duration_ms, padding_duration_ms, vad, frames):
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"""Filters out non-voiced audio frames.
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Given a webrtcvad.Vad and a source of audio frames, yields only
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the voiced audio.
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Uses a padded, sliding window algorithm over the audio frames.
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When more than 90% of the frames in the window are voiced (as
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reported by the VAD), the collector triggers and begins yielding
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audio frames. Then the collector waits until 90% of the frames in
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the window are unvoiced to detrigger.
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The window is padded at the front and back to provide a small
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amount of silence or the beginnings/endings of speech around the
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voiced frames.
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Arguments:
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sample_rate - The audio sample rate, in Hz.
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frame_duration_ms - The frame duration in milliseconds.
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padding_duration_ms - The amount to pad the window, in milliseconds.
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vad - An instance of webrtcvad.Vad.
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frames - a source of audio frames (sequence or generator).
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Returns: A generator that yields PCM audio data.
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"""
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num_padding_frames = int(padding_duration_ms / frame_duration_ms)
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# We use a deque for our sliding window/ring buffer.
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ring_buffer = collections.deque(maxlen=num_padding_frames)
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# We have two states: TRIGGERED and NOTTRIGGERED. We start in the
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# NOTTRIGGERED state.
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triggered = False
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voiced_frames = []
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for frame in frames:
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is_speech = vad.is_speech(frame.bytes, sample_rate)
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# sys.stdout.write('1' if is_speech else '0')
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if not triggered:
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ring_buffer.append((frame, is_speech))
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num_voiced = len([f for f, speech in ring_buffer if speech])
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# If we're NOTTRIGGERED and more than 90% of the frames in
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# the ring buffer are voiced frames, then enter the
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# TRIGGERED state.
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if num_voiced > 0.9 * ring_buffer.maxlen:
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triggered = True
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# sys.stdout.write('+(%s)' % (ring_buffer[0][0].timestamp,))
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# We want to yield all the audio we see from now until
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# we are NOTTRIGGERED, but we have to start with the
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# audio that's already in the ring buffer.
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for f, _ in ring_buffer:
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voiced_frames.append(f)
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ring_buffer.clear()
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else:
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# We're in the TRIGGERED state, so collect the audio data
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# and add it to the ring buffer.
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voiced_frames.append(frame)
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ring_buffer.append((frame, is_speech))
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num_unvoiced = len([f for f, speech in ring_buffer if not speech])
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# If more than 90% of the frames in the ring buffer are
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# unvoiced, then enter NOTTRIGGERED and yield whatever
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# audio we've collected.
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if num_unvoiced > 0.9 * ring_buffer.maxlen:
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# sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
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triggered = False
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yield b"".join([f.bytes for f in voiced_frames])
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ring_buffer.clear()
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voiced_frames = []
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# If we have any leftover voiced audio when we run out of input,
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# yield it.
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if voiced_frames:
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yield b"".join([f.bytes for f in voiced_frames])
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from TTS.utils.vad import read_wave, write_wave, get_vad_speech_segments
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def remove_silence(filepath):
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output_path = filepath.replace(os.path.join(args.input_dir, ""), os.path.join(args.output_dir, ""))
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# ignore if the file exists
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if os.path.exists(output_path) and not args.force:
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return
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# create all directory structure
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pathlib.Path(output_path).parent.mkdir(parents=True, exist_ok=True)
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padding_duration_ms = 300 # default 300
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# load wave
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audio, sample_rate = read_wave(filepath)
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vad = webrtcvad.Vad(int(args.aggressiveness))
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frames = frame_generator(30, audio, sample_rate)
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frames = list(frames)
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segments = vad_collector(sample_rate, 30, padding_duration_ms, vad, frames)
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flag = False
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# get speech segments
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segments = get_vad_speech_segments(audio, sample_rate, aggressiveness=args.aggressiveness)
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segments = list(segments)
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num_segments = len(segments)
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flag = False
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# create the output wave
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if num_segments != 0:
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for i, segment in reversed(list(enumerate(segments))):
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if i >= 1:
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@ -168,8 +37,8 @@ def remove_silence(filepath):
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else:
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if flag:
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segment = segment + concat_segment
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# print("Saving: ", output_path)
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write_wave(output_path, segment, sample_rate)
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print(output_path)
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return
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else:
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print("> Just Copying the file to:", output_path)
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@ -200,7 +69,10 @@ if __name__ == "__main__":
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parser.add_argument(
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"-o", "--output_dir", type=str, default="../VCTK-Corpus-removed-silence", help="Output Dataset dir"
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)
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parser.add_argument("-f", "--force", type=bool, default=True, help="Force the replace of exists files")
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parser.add_argument("-f", "--force",
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default=False,
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action='store_true',
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help='Force the replace of exists files')
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parser.add_argument(
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"-g",
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"--glob",
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@ -0,0 +1,142 @@
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# This code is adpated from: https://github.com/wiseman/py-webrtcvad/blob/master/example.py
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import wave
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import webrtcvad
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import contextlib
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import collections
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def read_wave(path):
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"""Reads a .wav file.
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Takes the path, and returns (PCM audio data, sample rate).
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"""
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with contextlib.closing(wave.open(path, "rb")) as wf:
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num_channels = wf.getnchannels()
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assert num_channels == 1
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sample_width = wf.getsampwidth()
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assert sample_width == 2
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sample_rate = wf.getframerate()
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assert sample_rate in (8000, 16000, 32000, 48000)
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pcm_data = wf.readframes(wf.getnframes())
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return pcm_data, sample_rate
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def write_wave(path, audio, sample_rate):
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"""Writes a .wav file.
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Takes path, PCM audio data, and sample rate.
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"""
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with contextlib.closing(wave.open(path, "wb")) as wf:
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wf.setnchannels(1)
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wf.setsampwidth(2)
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wf.setframerate(sample_rate)
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wf.writeframes(audio)
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class Frame(object):
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"""Represents a "frame" of audio data."""
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def __init__(self, _bytes, timestamp, duration):
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self.bytes =_bytes
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self.timestamp = timestamp
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self.duration = duration
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def frame_generator(frame_duration_ms, audio, sample_rate):
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"""Generates audio frames from PCM audio data.
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Takes the desired frame duration in milliseconds, the PCM data, and
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the sample rate.
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Yields Frames of the requested duration.
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"""
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n = int(sample_rate * (frame_duration_ms / 1000.0) * 2)
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offset = 0
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timestamp = 0.0
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duration = (float(n) / sample_rate) / 2.0
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while offset + n < len(audio):
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yield Frame(audio[offset : offset + n], timestamp, duration)
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timestamp += duration
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offset += n
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def vad_collector(sample_rate, frame_duration_ms, padding_duration_ms, vad, frames):
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"""Filters out non-voiced audio frames.
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Given a webrtcvad.Vad and a source of audio frames, yields only
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the voiced audio.
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Uses a padded, sliding window algorithm over the audio frames.
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When more than 90% of the frames in the window are voiced (as
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reported by the VAD), the collector triggers and begins yielding
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audio frames. Then the collector waits until 90% of the frames in
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the window are unvoiced to detrigger.
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The window is padded at the front and back to provide a small
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amount of silence or the beginnings/endings of speech around the
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voiced frames.
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Arguments:
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sample_rate - The audio sample rate, in Hz.
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frame_duration_ms - The frame duration in milliseconds.
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padding_duration_ms - The amount to pad the window, in milliseconds.
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vad - An instance of webrtcvad.Vad.
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frames - a source of audio frames (sequence or generator).
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Returns: A generator that yields PCM audio data.
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"""
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num_padding_frames = int(padding_duration_ms / frame_duration_ms)
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# We use a deque for our sliding window/ring buffer.
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ring_buffer = collections.deque(maxlen=num_padding_frames)
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# We have two states: TRIGGERED and NOTTRIGGERED. We start in the
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# NOTTRIGGERED state.
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triggered = False
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voiced_frames = []
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for frame in frames:
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is_speech = vad.is_speech(frame.bytes, sample_rate)
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# sys.stdout.write('1' if is_speech else '0')
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if not triggered:
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ring_buffer.append((frame, is_speech))
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num_voiced = len([f for f, speech in ring_buffer if speech])
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# If we're NOTTRIGGERED and more than 90% of the frames in
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# the ring buffer are voiced frames, then enter the
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# TRIGGERED state.
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if num_voiced > 0.9 * ring_buffer.maxlen:
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triggered = True
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# sys.stdout.write('+(%s)' % (ring_buffer[0][0].timestamp,))
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# We want to yield all the audio we see from now until
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# we are NOTTRIGGERED, but we have to start with the
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# audio that's already in the ring buffer.
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for f, _ in ring_buffer:
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voiced_frames.append(f)
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ring_buffer.clear()
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else:
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# We're in the TRIGGERED state, so collect the audio data
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# and add it to the ring buffer.
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voiced_frames.append(frame)
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ring_buffer.append((frame, is_speech))
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num_unvoiced = len([f for f, speech in ring_buffer if not speech])
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# If more than 90% of the frames in the ring buffer are
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# unvoiced, then enter NOTTRIGGERED and yield whatever
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# audio we've collected.
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if num_unvoiced > 0.9 * ring_buffer.maxlen:
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# sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
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triggered = False
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yield b"".join([f.bytes for f in voiced_frames])
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ring_buffer.clear()
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voiced_frames = []
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# If we have any leftover voiced audio when we run out of input,
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# yield it.
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if voiced_frames:
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yield b"".join([f.bytes for f in voiced_frames])
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def get_vad_speech_segments(audio, sample_rate, aggressiveness=2, padding_duration_ms=300):
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vad = webrtcvad.Vad(int(aggressiveness))
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frames = list(frame_generator(30, audio, sample_rate))
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segments = vad_collector(sample_rate, 30, padding_duration_ms, vad, frames)
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return segments
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