| import pandas as pd |
| import torchaudio |
| from glob import glob |
| import os |
| import csv |
| from tqdm import tqdm |
| import pandas |
|
|
| sentence_bounds = {'!', '.', ';', '?', '…'} |
|
|
| files = list(sorted(glob("earnings22/media/*.mp3"))) |
| metadata = [] |
| for audio_file in tqdm(files): |
| file_id = audio_file.split("/")[-1].split(".")[0] |
| nlp_file = f"earnings22/aligned/{file_id}.nlp" |
|
|
| speech, sr = torchaudio.load(audio_file) |
| with open(nlp_file, "r") as nlp: |
| start, end = None, None |
| sentence = "" |
| segment_id = 0 |
| csvreader = csv.DictReader(nlp, delimiter="|") |
| for row in csvreader: |
| punct = row["punctuation"].strip() |
| sentence += row["token"] |
| if punct: |
| sentence += punct |
| sentence += " " |
| if start is None and row["ts"].strip(): |
| start = float(row["ts"]) - 0.1 |
| if row["endTs"].strip(): |
| end = float(row["endTs"]) + 0.1 |
| if punct in sentence_bounds and start is not None and end is not None: |
| segment = speech[:, int(start*sr):int(end*sr)+1].contiguous() |
|
|
| os.makedirs(f"earnings22/segmented/{file_id}", exist_ok=True) |
| torchaudio.save(f"earnings22/segmented/{file_id}/{segment_id}.wav", |
| segment, sr, encoding="PCM_S", bits_per_sample=16) |
|
|
| data_row = { |
| "source_id": f"{file_id}", |
| "segment_id": segment_id, |
| "file": f"{file_id}/{segment_id}.wav", |
| "start_ts": start, |
| "end_ts": end, |
| "sentence": sentence.strip() |
| } |
| metadata.append(data_row) |
|
|
| start, end = None, None |
| sentence = "" |
| segment_id += 1 |
| pd.DataFrame(metadata).to_csv("earnings22/segmented/metadata.csv", index=False) |
|
|
|
|
