| | |
|---|
| | | wav_path = os.path.join(split_dir, str(i + 1), "wav.scp") |
|---|
| | | text_path = os.path.join(split_dir, str(i + 1), "text") |
|---|
| | | f_data.write(wav_path + " " + text_path + "\n") |
|---|
| | | |
|---|
| | | def filter_wav_text(data_dir, dataset): |
|---|
| | | wav_file = os.path.join(data_dir,dataset,"wav.scp") |
|---|
| | | text_file = os.path.join(data_dir, dataset, "text") |
|---|
| | | with open(wav_file) as f_wav, open(text_file) as f_text: |
|---|
| | | wav_lines = f_wav.readlines() |
|---|
| | | text_lines = f_text.readlines() |
|---|
| | | os.rename(wav_file, "{}.bak".format(wav_file)) |
|---|
| | | os.rename(text_file, "{}.bak".format(text_file)) |
|---|
| | | wav_dict = {} |
|---|
| | | for line in wav_lines: |
|---|
| | | parts = line.strip().split() |
|---|
| | | if len(parts) < 2: |
|---|
| | | continue |
|---|
| | | sample_name, wav_path = parts |
|---|
| | | wav_dict[sample_name] = wav_path |
|---|
| | | text_dict = {} |
|---|
| | | for line in text_lines: |
|---|
| | | parts = line.strip().split(" ", 1) |
|---|
| | | if len(parts) < 2: |
|---|
| | | continue |
|---|
| | | sample_name, txt = parts |
|---|
| | | text_dict[sample_name] = txt |
|---|
| | | filter_count = 0 |
|---|
| | | with open(wav_file, "w") as f_wav, open(text_file, "w") as f_text: |
|---|
| | | for sample_name, wav_path in wav_dict.items(): |
|---|
| | | if sample_name in text_dict.keys(): |
|---|
| | | f_wav.write(sample_name + " " + wav_path + "\n") |
|---|
| | | f_text.write(sample_name + " " + text_dict[sample_name] + "\n") |
|---|
| | | else: |
|---|
| | | filter_count += 1 |
|---|
| | | print("{}/{} samples in {} are filtered because of the mismatch between wav.scp and text".format(len(wav_lines), filter_count, dataset)) |
|---|
