Datasets:
Tasks:
Automatic Speech Recognition
Formats:
parquet
Languages:
English
Size:
100K - 1M
ArXiv:
License:
| #!/usr/bin/env python3 | |
| import os | |
| import torchaudio | |
| import tqdm | |
| SAMPLE_RATE = 16_000 | |
| path = "/home/patrick/kaldi/egs/ami/s5/mdm_downloaded/{folder}/audio/{folder}.Array1-01.wav" | |
| new_path = "/home/patrick/ami/audio/sdm" | |
| for split in ["train", "dev", "eval"]: | |
| new_split_path = os.path.join(new_path, split) | |
| audio_chunks_path = os.path.join("/home/patrick/ami/annotations/", split, "segments") | |
| files = {} | |
| with open(audio_chunks_path, "r") as f: | |
| lines = f.readlines() | |
| for line in lines: | |
| file_name, folder, start_time, end_time = line.strip().split() | |
| folder = folder.split("_")[1] | |
| os.system(f"mkdir -p {os.path.join(new_split_path, folder)}") | |
| if folder not in files: | |
| files[folder] = [] | |
| files[folder].append((file_name, start_time, end_time)) | |
| for folder, audios in tqdm.tqdm(files.items()): | |
| orig_file = path.format(folder=folder) | |
| try: | |
| waveform, sr = torchaudio.load(orig_file) | |
| except: | |
| print(f"File {orig_file} does not exist!") | |
| continue | |
| # for file_name, start_time, end_time in audios: | |
| # chunk = waveform[:, int(SAMPLE_RATE * float(start_time)): int(SAMPLE_RATE * float(end_time))] | |
| # out_path = f"{split}_{file_name.lower().replace('h00', 'sdm')}.wav" | |
| # out_path = os.path.join(new_split_path, folder, out_path) | |
| # torchaudio.save(out_path, chunk, sr) | |
| abs_folder = os.path.join(new_split_path, folder) | |
| os.system(f"cd {new_split_path} && tar -czf {folder}.tar.gz {folder}") | |