# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import logging import os import sys import time from pathlib import Path import numpy as np import scipy.io.wavfile as wav import torch from joblib import Parallel, delayed from tqdm import tqdm from utils import get_segments import nemo.collections.asr as nemo_asr from nemo.collections.asr.models.ctc_models import EncDecCTCModel from nemo.collections.asr.models.hybrid_rnnt_ctc_models import EncDecHybridRNNTCTCModel parser = argparse.ArgumentParser(description="CTC Segmentation") parser.add_argument("--output_dir", default="output", type=str, help="Path to output directory") parser.add_argument( "--data", type=str, required=True, help="Path to directory with audio files and associated transcripts (same respective names only formats are " "different or path to wav file (transcript should have the same base name and be located in the same folder" "as the wav file.", ) parser.add_argument("--window_len", type=int, default=8000, help="Window size for ctc segmentation algorithm") parser.add_argument("--sample_rate", type=int, default=16000, help="Sampling rate, Hz") parser.add_argument( "--model", type=str, default="QuartzNet15x5Base-En", help="Path to model checkpoint or pre-trained model name", ) parser.add_argument("--debug", action="store_true", help="Flag to enable debugging messages") parser.add_argument( "--num_jobs", default=-2, type=int, help="The maximum number of concurrently running jobs, `-2` - all CPUs but one are used", ) logger = logging.getLogger("ctc_segmentation") # use module name if __name__ == "__main__": args = parser.parse_args() logging.basicConfig(level=logging.INFO) # setup logger log_dir = os.path.join(args.output_dir, "logs") os.makedirs(log_dir, exist_ok=True) log_file = os.path.join(log_dir, f"ctc_segmentation_{args.window_len}.log") if os.path.exists(log_file): os.remove(log_file) level = "DEBUG" if args.debug else "INFO" logger = logging.getLogger("CTC") file_handler = logging.FileHandler(filename=log_file) stdout_handler = logging.StreamHandler(sys.stdout) handlers = [file_handler, stdout_handler] logging.basicConfig(handlers=handlers, level=level) if os.path.exists(args.model): asr_model = nemo_asr.models.ASRModel.restore_from(args.model) else: asr_model = nemo_asr.models.ASRModel.from_pretrained(args.model, strict=False) if not (isinstance(asr_model, EncDecCTCModel) or isinstance(asr_model, EncDecHybridRNNTCTCModel)): raise NotImplementedError( f"Model is not an instance of NeMo EncDecCTCModel or ENCDecHybridRNNTCTCModel." " Currently only instances of these models are supported" ) bpe_model = isinstance(asr_model, nemo_asr.models.EncDecCTCModelBPE) or isinstance( asr_model, nemo_asr.models.EncDecHybridRNNTCTCBPEModel ) # get tokenizer used during training, None for char based models if bpe_model: tokenizer = asr_model.tokenizer else: tokenizer = None if isinstance(asr_model, EncDecHybridRNNTCTCModel): asr_model.change_decoding_strategy(decoder_type="ctc") # extract ASR vocabulary and add blank symbol if hasattr(asr_model, 'tokenizer'): # i.e. tokenization is BPE-based vocabulary = asr_model.tokenizer.vocab elif hasattr(asr_model.decoder, "vocabulary"): # i.e. tokenization is character-based vocabulary = asr_model.cfg.decoder.vocabulary else: raise ValueError("Unexpected model type. Vocabulary list not found.") vocabulary = ["ε"] + list(vocabulary) logging.debug(f"ASR Model vocabulary: {vocabulary}") data = Path(args.data) output_dir = Path(args.output_dir) if os.path.isdir(data): audio_paths = data.glob("*.wav") data_dir = data else: audio_paths = [Path(data)] data_dir = Path(os.path.dirname(data)) all_log_probs = [] all_transcript_file = [] all_segment_file = [] all_wav_paths = [] segments_dir = os.path.join(args.output_dir, "segments") os.makedirs(segments_dir, exist_ok=True) index_duration = None for path_audio in audio_paths: logging.info(f"Processing {path_audio.name}...") transcript_file = os.path.join(data_dir, path_audio.name.replace(".wav", ".txt")) segment_file = os.path.join( segments_dir, f"{args.window_len}_" + path_audio.name.replace(".wav", "_segments.txt") ) if not os.path.exists(transcript_file): logging.info(f"{transcript_file} not found. Skipping {path_audio.name}") continue try: sample_rate, signal = wav.read(path_audio) if len(signal) == 0: logging.error(f"Skipping {path_audio.name}") continue assert ( sample_rate == args.sample_rate ), f"Sampling rate of the audio file {path_audio} doesn't match --sample_rate={args.sample_rate}" original_duration = len(signal) / sample_rate logging.debug(f"len(signal): {len(signal)}, sr: {sample_rate}") logging.debug(f"Duration: {original_duration}s, file_name: {path_audio}") hypotheses = asr_model.transcribe([str(path_audio)], batch_size=1, return_hypotheses=True) # if hypotheses form a tuple (from Hybrid model), extract just "best" hypothesis if type(hypotheses) == tuple and len(hypotheses) == 2: hypotheses = hypotheses[0] log_probs = hypotheses[ 0 ].alignments # note: "[0]" is for batch dimension unpacking (and here batch size=1) # move blank values to the first column (ctc-package compatibility) blank_col = log_probs[:, -1].reshape((log_probs.shape[0], 1)) log_probs = np.concatenate((blank_col, log_probs[:, :-1]), axis=1) all_log_probs.append(log_probs) all_segment_file.append(str(segment_file)) all_transcript_file.append(str(transcript_file)) all_wav_paths.append(path_audio) if index_duration is None: index_duration = len(signal) / log_probs.shape[0] / sample_rate except Exception as e: logging.error(e) logging.error(f"Skipping {path_audio.name}") continue asr_model_type = type(asr_model) del asr_model torch.cuda.empty_cache() if len(all_log_probs) > 0: start_time = time.time() normalized_lines = Parallel(n_jobs=args.num_jobs)( delayed(get_segments)( all_log_probs[i], all_wav_paths[i], all_transcript_file[i], all_segment_file[i], vocabulary, tokenizer, bpe_model, index_duration, args.window_len, log_file=log_file, debug=args.debug, ) for i in tqdm(range(len(all_log_probs))) ) total_time = time.time() - start_time logger.info(f"Total execution time: ~{round(total_time/60)}min") logger.info(f"Saving logs to {log_file}") if os.path.exists(log_file): with open(log_file, "r") as f: lines = f.readlines()