import time
import numpy as np
import torch
import torch.nn.functional as F
from transformers import AutoModelForCTC, AutoProcessor
import pyaudio
import soundfile as sf
import resampy
from queue import Queue
from threading import Thread, Event
def _read_frame(stream, exit_event, queue, chunk):
while True:
if exit_event.is_set():
print(f'[INFO] read frame thread ends')
break
frame = stream.read(chunk, exception_on_overflow=False)
frame = np.frombuffer(frame, dtype=np.int16).astype(np.float32) / 32767 # [chunk]
queue.put(frame)
def _play_frame(stream, exit_event, queue, chunk):
while True:
if exit_event.is_set():
print(f'[INFO] play frame thread ends')
break
frame = queue.get()
frame = (frame * 32767).astype(np.int16).tobytes()
stream.write(frame, chunk)
class ASR:
def __init__(self, opt):
self.opt = opt
self.play = opt.asr_play
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.fps = opt.fps # 20 ms per frame
self.sample_rate = 16000
self.chunk = self.sample_rate // self.fps # 320 samples per chunk (20ms * 16000 / 1000)
self.mode = 'live' if opt.asr_wav == '' else 'file'
if 'esperanto' in self.opt.asr_model:
self.audio_dim = 44
elif 'deepspeech' in self.opt.asr_model:
self.audio_dim = 29
else:
self.audio_dim = 32
# prepare context cache
# each segment is (stride_left + ctx + stride_right) * 20ms, latency should be (ctx + stride_right) * 20ms
self.context_size = opt.m
self.stride_left_size = opt.l
self.stride_right_size = opt.r
self.text = '[START]\n'
self.terminated = False
self.frames = []
# pad left frames
if self.stride_left_size > 0:
self.frames.extend([np.zeros(self.chunk, dtype=np.float32)] * self.stride_left_size)
self.exit_event = Event()
self.audio_instance = pyaudio.PyAudio()
# create input stream
if self.mode == 'file':
self.file_stream = self.create_file_stream()
else:
# start a background process to read frames
self.input_stream = self.audio_instance.open(format=pyaudio.paInt16, channels=1, rate=self.sample_rate, input=True, output=False, frames_per_buffer=self.chunk)
self.queue = Queue()
self.process_read_frame = Thread(target=_read_frame, args=(self.input_stream, self.exit_event, self.queue, self.chunk))
# play out the audio too...?
if self.play:
self.output_stream = self.audio_instance.open(format=pyaudio.paInt16, channels=1, rate=self.sample_rate, input=False, output=True, frames_per_buffer=self.chunk)
self.output_queue = Queue()
self.process_play_frame = Thread(target=_play_frame, args=(self.output_stream, self.exit_event, self.output_queue, self.chunk))
# current location of audio
self.idx = 0
# create wav2vec model
print(f'[INFO] loading ASR model {self.opt.asr_model}...')
self.processor = AutoProcessor.from_pretrained(opt.asr_model)
self.model = AutoModelForCTC.from_pretrained(opt.asr_model).to(self.device)
# prepare to save logits
if self.opt.asr_save_feats:
self.all_feats = []
# the extracted features
# use a loop queue to efficiently record endless features: [f--t---][-------][-------]
self.feat_buffer_size = 4
self.feat_buffer_idx = 0
self.feat_queue = torch.zeros(self.feat_buffer_size * self.context_size, self.audio_dim, dtype=torch.float32, device=self.device)
# TODO: hard coded 16 and 8 window size...
self.front = self.feat_buffer_size * self.context_size - 8 # fake padding
self.tail = 8
# attention window...
self.att_feats = [torch.zeros(self.audio_dim, 16, dtype=torch.float32, device=self.device)] * 4 # 4 zero padding...
# warm up steps needed: mid + right + window_size + attention_size
self.warm_up_steps = self.context_size + self.stride_right_size + 8 + 2 * 3
self.listening = False
self.playing = False
def listen(self):
# start
if self.mode == 'live' and not self.listening:
print(f'[INFO] starting read frame thread...')
self.process_read_frame.start()
self.listening = True
if self.play and not self.playing:
print(f'[INFO] starting play frame thread...')
self.process_play_frame.start()
self.playing = True
def stop(self):
self.exit_event.set()
if self.play:
self.output_stream.stop_stream()
self.output_stream.close()
if self.playing:
self.process_play_frame.join()
self.playing = False
if self.mode == 'live':
self.input_stream.stop_stream()
self.input_stream.close()
if self.listening:
self.process_read_frame.join()
self.listening = False
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.stop()
if self.mode == 'live':
# live mode: also print the result text.
self.text += '\n[END]'
print(self.text)
def get_next_feat(self):
# return a [1/8, 16] window, for the next input to nerf side.
while len(self.att_feats) < 8:
# [------f+++t-----]
if self.front < self.tail:
feat = self.feat_queue[self.front:self.tail]
# [++t-----------f+]
else:
feat = torch.cat([self.feat_queue[self.front:], self.feat_queue[:self.tail]], dim=0)
self.front = (self.front + 2) % self.feat_queue.shape[0]
self.tail = (self.tail + 2) % self.feat_queue.shape[0]
# print(self.front, self.tail, feat.shape)
self.att_feats.append(feat.permute(1, 0))
att_feat = torch.stack(self.att_feats, dim=0) # [8, 44, 16]
# discard old
self.att_feats = self.att_feats[1:]
return att_feat
def run_step(self):
if self.terminated:
return
# get a frame of audio
frame = self.get_audio_frame()
# the last frame
if frame is None:
# terminate, but always run the network for the left frames
self.terminated = True
else:
self.frames.append(frame)
# put to output
if self.play:
self.output_queue.put(frame)
# context not enough, do not run network.
if len(self.frames) < self.stride_left_size + self.context_size + self.stride_right_size:
return
inputs = np.concatenate(self.frames) # [N * chunk]
# discard the old part to save memory
if not self.terminated:
self.frames = self.frames[-(self.stride_left_size + self.stride_right_size):]
logits, labels, text = self.frame_to_text(inputs)
feats = logits # better lips-sync than labels
# save feats
if self.opt.asr_save_feats:
self.all_feats.append(feats)
# record the feats efficiently.. (no concat, constant memory)
if not self.terminated:
start = self.feat_buffer_idx * self.context_size
end = start + feats.shape[0]
self.feat_queue[start:end] = feats
self.feat_buffer_idx = (self.feat_buffer_idx + 1) % self.feat_buffer_size
# very naive, just concat the text output.
if text != '':
self.text = self.text + ' ' + text
# will only run once at ternimation
if self.terminated:
self.text += '\n[END]'
# print(self.text)
if self.opt.asr_save_feats:
print(f'[INFO] save all feats for training purpose... ')
feats = torch.cat(self.all_feats, dim=0) # [N, C]
# print('[INFO] before unfold', feats.shape)
window_size = 16
padding = window_size // 2
feats = feats.view(-1, self.audio_dim).permute(1, 0).contiguous() # [C, M]
feats = feats.view(1, self.audio_dim, -1, 1) # [1, C, M, 1]
unfold_feats = F.unfold(feats, kernel_size=(window_size, 1), padding=(padding, 0), stride=(2, 1)) # [1, C * window_size, M / 2 + 1]
unfold_feats = unfold_feats.view(self.audio_dim, window_size, -1).permute(2, 1, 0).contiguous() # [C, window_size, M / 2 + 1] --> [M / 2 + 1, window_size, C]
# print('[INFO] after unfold', unfold_feats.shape)
# save to a npy file
if 'esperanto' in self.opt.asr_model:
output_path = self.opt.asr_wav.replace('.wav', '_eo.npy')
else:
output_path = self.opt.asr_wav.replace('.wav', '.npy')
np.save(output_path, unfold_feats.cpu().numpy())
print(f"[INFO] saved logits to {output_path}")
def create_file_stream(self):
stream, sample_rate = sf.read(self.opt.asr_wav) # [T*sample_rate,] float64
stream = stream.astype(np.float32)
if stream.ndim > 1:
print(f'[WARN] audio has {stream.shape[1]} channels, only use the first.')
stream = stream[:, 0]
if sample_rate != self.sample_rate:
print(f'[WARN] audio sample rate is {sample_rate}, resampling into {self.sample_rate}.')
stream = resampy.resample(x=stream, sr_orig=sample_rate, sr_new=self.sample_rate)
print(f'[INFO] loaded audio stream {self.opt.asr_wav}: {stream.shape}')
return stream
def create_pyaudio_stream(self):
import pyaudio
print(f'[INFO] creating live audio stream ...')
audio = pyaudio.PyAudio()
# get devices
info = audio.get_host_api_info_by_index(0)
n_devices = info.get('deviceCount')
for i in range(0, n_devices):
if (audio.get_device_info_by_host_api_device_index(0, i).get('maxInputChannels')) > 0:
name = audio.get_device_info_by_host_api_device_index(0, i).get('name')
print(f'[INFO] choose audio device {name}, id {i}')
break
# get stream
stream = audio.open(input_device_index=i,
format=pyaudio.paInt16,
channels=1,
rate=self.sample_rate,
input=True,
frames_per_buffer=self.chunk)
return audio, stream
def get_audio_frame(self):
if self.mode == 'file':
if self.idx < self.file_stream.shape[0]:
frame = self.file_stream[self.idx: self.idx + self.chunk]
self.idx = self.idx + self.chunk
return frame
else:
return None
else:
frame = self.queue.get()
# print(f'[INFO] get frame {frame.shape}')
self.idx = self.idx + self.chunk
return frame
def frame_to_text(self, frame):
# frame: [N * 320], N = (context_size + 2 * stride_size)
inputs = self.processor(frame, sampling_rate=self.sample_rate, return_tensors="pt", padding=True)
with torch.no_grad():
result = self.model(inputs.input_values.to(self.device))
logits = result.logits # [1, N - 1, 32]
# cut off stride
left = max(0, self.stride_left_size)
right = min(logits.shape[1], logits.shape[1] - self.stride_right_size + 1) # +1 to make sure output is the same length as input.
# do not cut right if terminated.
if self.terminated:
right = logits.shape[1]
logits = logits[:, left:right]
# print(frame.shape, inputs.input_values.shape, logits.shape)
predicted_ids = torch.argmax(logits, dim=-1)
transcription = self.processor.batch_decode(predicted_ids)[0].lower()
# for esperanto
# labels = np.array(['ŭ', '»', 'c', 'ĵ', 'ñ', '”', '„', '“', 'ǔ', 'o', 'ĝ', 'm', 'k', 'd', 'a', 'ŝ', 'z', 'i', '«', '—', '‘', 'ĥ', 'f', 'y', 'h', 'j', '|', 'r', 'u', 'ĉ', 's', '–', 'fi', 'l', 'p', '’', 'g', 'v', 't', 'b', 'n', 'e', '[UNK]', '[PAD]'])
# labels = np.array([' ', ' ', ' ', '-', '|', 'E', 'T', 'A', 'O', 'N', 'I', 'H', 'S', 'R', 'D', 'L', 'U', 'M', 'W', 'C', 'F', 'G', 'Y', 'P', 'B', 'V', 'K', "'", 'X', 'J', 'Q', 'Z'])
# print(''.join(labels[predicted_ids[0].detach().cpu().long().numpy()]))
# print(predicted_ids[0])
# print(transcription)
return logits[0], predicted_ids[0], transcription # [N,]
def run(self):
self.listen()
while not self.terminated:
self.run_step()
def clear_queue(self):
# clear the queue, to reduce potential latency...
print(f'[INFO] clear queue')
if self.mode == 'live':
self.queue.queue.clear()
if self.play:
self.output_queue.queue.clear()
def warm_up(self):
self.listen()
print(f'[INFO] warm up ASR live model, expected latency = {self.warm_up_steps / self.fps:.6f}s')
t = time.time()
for _ in range(self.warm_up_steps):
self.run_step()
if torch.cuda.is_available():
torch.cuda.synchronize()
t = time.time() - t
print(f'[INFO] warm-up done, actual latency = {t:.6f}s')
self.clear_queue()
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--wav', type=str, default='')
parser.add_argument('--play', action='store_true', help="play out the audio")
parser.add_argument('--model', type=str, default='cpierse/wav2vec2-large-xlsr-53-esperanto')
# parser.add_argument('--model', type=str, default='facebook/wav2vec2-large-960h-lv60-self')
parser.add_argument('--save_feats', action='store_true')
# audio FPS
parser.add_argument('--fps', type=int, default=50)
# sliding window left-middle-right length.
parser.add_argument('-l', type=int, default=10)
parser.add_argument('-m', type=int, default=50)
parser.add_argument('-r', type=int, default=10)
opt = parser.parse_args()
# fix
opt.asr_wav = opt.wav
opt.asr_play = opt.play
opt.asr_model = opt.model
opt.asr_save_feats = opt.save_feats
if 'deepspeech' in opt.asr_model:
raise ValueError("DeepSpeech features should not use this code to extract...")
with ASR(opt) as asr:
asr.run()