| 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) | |