487 lines
18 KiB
Python
487 lines
18 KiB
Python
import time
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import numpy as np
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import torch
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import torch.nn.functional as F
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from transformers import AutoModelForCTC, AutoProcessor
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#import pyaudio
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import soundfile as sf
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import resampy
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import queue
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from queue import Queue
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#from collections import deque
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from threading import Thread, Event
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from io import BytesIO
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def _read_frame(stream, exit_event, queue, chunk):
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while True:
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if exit_event.is_set():
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print(f'[INFO] read frame thread ends')
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break
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frame = stream.read(chunk, exception_on_overflow=False)
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frame = np.frombuffer(frame, dtype=np.int16).astype(np.float32) / 32767 # [chunk]
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queue.put(frame)
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def _play_frame(stream, exit_event, queue, chunk):
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while True:
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if exit_event.is_set():
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print(f'[INFO] play frame thread ends')
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break
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frame = queue.get()
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frame = (frame * 32767).astype(np.int16).tobytes()
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stream.write(frame, chunk)
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class ASR:
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def __init__(self, opt):
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self.opt = opt
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self.play = opt.asr_play #false
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self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
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self.fps = opt.fps # 20 ms per frame
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self.sample_rate = 16000
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self.chunk = self.sample_rate // self.fps # 320 samples per chunk (20ms * 16000 / 1000)
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self.mode = 'live' if opt.asr_wav == '' else 'file'
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if 'esperanto' in self.opt.asr_model:
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self.audio_dim = 44
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elif 'deepspeech' in self.opt.asr_model:
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self.audio_dim = 29
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else:
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self.audio_dim = 32
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# prepare context cache
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# each segment is (stride_left + ctx + stride_right) * 20ms, latency should be (ctx + stride_right) * 20ms
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self.context_size = opt.m
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self.stride_left_size = opt.l
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self.stride_right_size = opt.r
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self.text = '[START]\n'
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self.terminated = False
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self.frames = []
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self.inwarm = False
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# pad left frames
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if self.stride_left_size > 0:
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self.frames.extend([np.zeros(self.chunk, dtype=np.float32)] * self.stride_left_size)
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self.exit_event = Event()
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#self.audio_instance = pyaudio.PyAudio() #not need
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# create input stream
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if self.mode == 'file': #live mode
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self.file_stream = self.create_file_stream()
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else:
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self.queue = Queue()
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self.input_stream = BytesIO()
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self.output_queue = Queue()
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# start a background process to read frames
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#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)
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#self.queue = Queue()
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#self.process_read_frame = Thread(target=_read_frame, args=(self.input_stream, self.exit_event, self.queue, self.chunk))
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# play out the audio too...?
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if self.play:
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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)
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self.output_queue = Queue()
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self.process_play_frame = Thread(target=_play_frame, args=(self.output_stream, self.exit_event, self.output_queue, self.chunk))
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# current location of audio
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self.idx = 0
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# create wav2vec model
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print(f'[INFO] loading ASR model {self.opt.asr_model}...')
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self.processor = AutoProcessor.from_pretrained(opt.asr_model)
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self.model = AutoModelForCTC.from_pretrained(opt.asr_model).to(self.device)
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# prepare to save logits
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if self.opt.asr_save_feats:
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self.all_feats = []
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# the extracted features
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# use a loop queue to efficiently record endless features: [f--t---][-------][-------]
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self.feat_buffer_size = 4
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self.feat_buffer_idx = 0
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self.feat_queue = torch.zeros(self.feat_buffer_size * self.context_size, self.audio_dim, dtype=torch.float32, device=self.device)
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# TODO: hard coded 16 and 8 window size...
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self.front = self.feat_buffer_size * self.context_size - 8 # fake padding
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self.tail = 8
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# attention window...
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self.att_feats = [torch.zeros(self.audio_dim, 16, dtype=torch.float32, device=self.device)] * 4 # 4 zero padding...
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# warm up steps needed: mid + right + window_size + attention_size
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self.warm_up_steps = self.context_size + self.stride_right_size + self.stride_left_size #+ 8 + 2 * 3
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self.listening = False
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self.playing = False
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def listen(self):
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# start
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if self.mode == 'live' and not self.listening:
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print(f'[INFO] starting read frame thread...')
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self.process_read_frame.start()
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self.listening = True
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if self.play and not self.playing:
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print(f'[INFO] starting play frame thread...')
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self.process_play_frame.start()
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self.playing = True
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def stop(self):
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self.exit_event.set()
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if self.play:
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self.output_stream.stop_stream()
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self.output_stream.close()
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if self.playing:
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self.process_play_frame.join()
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self.playing = False
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if self.mode == 'live':
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#self.input_stream.stop_stream() todo
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self.input_stream.close()
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if self.listening:
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self.process_read_frame.join()
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self.listening = False
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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self.stop()
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if self.mode == 'live':
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# live mode: also print the result text.
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self.text += '\n[END]'
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print(self.text)
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def get_next_feat(self):
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# return a [1/8, 16] window, for the next input to nerf side.
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while len(self.att_feats) < 8:
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# [------f+++t-----]
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if self.front < self.tail:
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feat = self.feat_queue[self.front:self.tail]
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# [++t-----------f+]
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else:
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feat = torch.cat([self.feat_queue[self.front:], self.feat_queue[:self.tail]], dim=0)
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self.front = (self.front + 2) % self.feat_queue.shape[0]
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self.tail = (self.tail + 2) % self.feat_queue.shape[0]
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# print(self.front, self.tail, feat.shape)
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self.att_feats.append(feat.permute(1, 0))
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att_feat = torch.stack(self.att_feats, dim=0) # [8, 44, 16]
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# discard old
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self.att_feats = self.att_feats[1:]
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return att_feat
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def run_step(self):
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if self.terminated:
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return
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# get a frame of audio
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frame = self.get_audio_frame()
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# the last frame
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if frame is None:
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# terminate, but always run the network for the left frames
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self.terminated = True
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else:
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self.frames.append(frame)
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# put to output
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self.output_queue.put(frame)
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# context not enough, do not run network.
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if len(self.frames) < self.stride_left_size + self.context_size + self.stride_right_size:
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return
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inputs = np.concatenate(self.frames) # [N * chunk]
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# discard the old part to save memory
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if not self.terminated:
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self.frames = self.frames[-(self.stride_left_size + self.stride_right_size):]
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print(f'[INFO] frame_to_text... ')
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#t = time.time()
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logits, labels, text = self.frame_to_text(inputs)
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#print(f'-------wav2vec time:{time.time()-t:.4f}s')
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feats = logits # better lips-sync than labels
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# save feats
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if self.opt.asr_save_feats:
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self.all_feats.append(feats)
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# record the feats efficiently.. (no concat, constant memory)
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start = self.feat_buffer_idx * self.context_size
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end = start + feats.shape[0]
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self.feat_queue[start:end] = feats
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self.feat_buffer_idx = (self.feat_buffer_idx + 1) % self.feat_buffer_size
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# very naive, just concat the text output.
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#if text != '':
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# self.text = self.text + ' ' + text
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# will only run once at ternimation
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if self.terminated:
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self.text += '\n[END]'
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print(self.text)
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if self.opt.asr_save_feats:
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print(f'[INFO] save all feats for training purpose... ')
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feats = torch.cat(self.all_feats, dim=0) # [N, C]
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# print('[INFO] before unfold', feats.shape)
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window_size = 16
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padding = window_size // 2
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feats = feats.view(-1, self.audio_dim).permute(1, 0).contiguous() # [C, M]
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feats = feats.view(1, self.audio_dim, -1, 1) # [1, C, M, 1]
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unfold_feats = F.unfold(feats, kernel_size=(window_size, 1), padding=(padding, 0), stride=(2, 1)) # [1, C * window_size, M / 2 + 1]
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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]
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# print('[INFO] after unfold', unfold_feats.shape)
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# save to a npy file
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if 'esperanto' in self.opt.asr_model:
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output_path = self.opt.asr_wav.replace('.wav', '_eo.npy')
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else:
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output_path = self.opt.asr_wav.replace('.wav', '.npy')
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np.save(output_path, unfold_feats.cpu().numpy())
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print(f"[INFO] saved logits to {output_path}")
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'''
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def create_file_stream(self):
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stream, sample_rate = sf.read(self.opt.asr_wav) # [T*sample_rate,] float64
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stream = stream.astype(np.float32)
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if stream.ndim > 1:
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print(f'[WARN] audio has {stream.shape[1]} channels, only use the first.')
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stream = stream[:, 0]
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if sample_rate != self.sample_rate:
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print(f'[WARN] audio sample rate is {sample_rate}, resampling into {self.sample_rate}.')
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stream = resampy.resample(x=stream, sr_orig=sample_rate, sr_new=self.sample_rate)
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print(f'[INFO] loaded audio stream {self.opt.asr_wav}: {stream.shape}')
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return stream
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def create_pyaudio_stream(self):
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import pyaudio
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print(f'[INFO] creating live audio stream ...')
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audio = pyaudio.PyAudio()
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# get devices
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info = audio.get_host_api_info_by_index(0)
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n_devices = info.get('deviceCount')
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for i in range(0, n_devices):
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if (audio.get_device_info_by_host_api_device_index(0, i).get('maxInputChannels')) > 0:
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name = audio.get_device_info_by_host_api_device_index(0, i).get('name')
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print(f'[INFO] choose audio device {name}, id {i}')
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break
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# get stream
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stream = audio.open(input_device_index=i,
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format=pyaudio.paInt16,
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channels=1,
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rate=self.sample_rate,
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input=True,
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frames_per_buffer=self.chunk)
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return audio, stream
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'''
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def get_audio_frame(self):
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if self.inwarm: # warm up
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return np.zeros(self.chunk, dtype=np.float32)
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if self.mode == 'file':
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if self.idx < self.file_stream.shape[0]:
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frame = self.file_stream[self.idx: self.idx + self.chunk]
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self.idx = self.idx + self.chunk
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return frame
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else:
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return None
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else:
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try:
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frame = self.queue.get(block=False)
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print(f'[INFO] get frame {frame.shape}')
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except queue.Empty:
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frame = np.zeros(self.chunk, dtype=np.float32)
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self.idx = self.idx + self.chunk
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return frame
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def frame_to_text(self, frame):
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# frame: [N * 320], N = (context_size + 2 * stride_size)
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inputs = self.processor(frame, sampling_rate=self.sample_rate, return_tensors="pt", padding=True)
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with torch.no_grad():
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result = self.model(inputs.input_values.to(self.device))
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logits = result.logits # [1, N - 1, 32]
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# cut off stride
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left = max(0, self.stride_left_size)
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right = min(logits.shape[1], logits.shape[1] - self.stride_right_size + 1) # +1 to make sure output is the same length as input.
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# do not cut right if terminated.
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if self.terminated:
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right = logits.shape[1]
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logits = logits[:, left:right]
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# print(frame.shape, inputs.input_values.shape, logits.shape)
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#predicted_ids = torch.argmax(logits, dim=-1)
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#transcription = self.processor.batch_decode(predicted_ids)[0].lower()
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# for esperanto
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# 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]'])
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# 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'])
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# print(''.join(labels[predicted_ids[0].detach().cpu().long().numpy()]))
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# print(predicted_ids[0])
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# print(transcription)
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return logits[0], None,None #predicted_ids[0], transcription # [N,]
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def create_bytes_stream(self,byte_stream):
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#byte_stream=BytesIO(buffer)
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stream, sample_rate = sf.read(byte_stream) # [T*sample_rate,] float64
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print(f'[INFO]tts audio stream {sample_rate}: {stream.shape}')
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stream = stream.astype(np.float32)
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if stream.ndim > 1:
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print(f'[WARN] audio has {stream.shape[1]} channels, only use the first.')
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stream = stream[:, 0]
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if sample_rate != self.sample_rate and stream.shape[0]>0:
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print(f'[WARN] audio sample rate is {sample_rate}, resampling into {self.sample_rate}.')
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stream = resampy.resample(x=stream, sr_orig=sample_rate, sr_new=self.sample_rate)
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return stream
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def push_audio(self,buffer):
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print(f'[INFO] push_audio {len(buffer)}')
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if len(buffer)>0:
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if self.opt.tts == "xtts":
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stream = np.frombuffer(buffer, dtype=np.int16).astype(np.float32) / 32767
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#stream = buffer.astype(np.float32)
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stream = resampy.resample(x=stream, sr_orig=24000, sr_new=self.sample_rate)
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else:
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byte_stream=BytesIO(buffer)
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stream = self.create_bytes_stream(byte_stream)
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streamlen = stream.shape[0]
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idx=0
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while streamlen >= self.chunk:
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self.queue.put(stream[idx:idx+self.chunk])
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streamlen -= self.chunk
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idx += self.chunk
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# if streamlen>0: #skip last frame(not 20ms)
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# self.queue.put(stream[idx:])
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# self.input_stream.write(buffer)
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# if len(buffer)<=0:
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# self.input_stream.seek(0)
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# stream = self.create_bytes_stream(self.input_stream)
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# streamlen = stream.shape[0]
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# idx=0
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# while streamlen >= self.chunk:
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# self.queue.put(stream[idx:idx+self.chunk])
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# streamlen -= self.chunk
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# idx += self.chunk
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# #if streamlen>0: #skip last frame(not 20ms)
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# # self.queue.put(stream[idx:])
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# self.input_stream.seek(0)
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# self.input_stream.truncate()
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def get_audio_out(self):
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return self.output_queue.get()
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def run(self):
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self.listen()
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while not self.terminated:
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self.run_step()
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def clear_queue(self):
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# clear the queue, to reduce potential latency...
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print(f'[INFO] clear queue')
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if self.mode == 'live':
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self.queue.queue.clear()
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if self.play:
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self.output_queue.queue.clear()
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def warm_up(self):
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#self.listen()
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self.inwarm = True
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print(f'[INFO] warm up ASR live model, expected latency = {self.warm_up_steps / self.fps:.6f}s')
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t = time.time()
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for _ in range(self.warm_up_steps):
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self.run_step()
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if torch.cuda.is_available():
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torch.cuda.synchronize()
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t = time.time() - t
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print(f'[INFO] warm-up done, actual latency = {t:.6f}s')
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self.inwarm = False
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#self.clear_queue()
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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('--wav', type=str, default='')
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parser.add_argument('--play', action='store_true', help="play out the audio")
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parser.add_argument('--model', type=str, default='cpierse/wav2vec2-large-xlsr-53-esperanto')
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# parser.add_argument('--model', type=str, default='facebook/wav2vec2-large-960h-lv60-self')
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parser.add_argument('--save_feats', action='store_true')
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# audio FPS
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parser.add_argument('--fps', type=int, default=50)
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||
# 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() |