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557ce40 894156f 557ce40 df8e593 557ce40 f68506f 557ce40 7a01aa9 557ce40 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | import datasets
import csv
import json
import os
import csv
_DATA_URL = "https://huggingface.co/datasets/fanfanchn/ASR-SECoMiCSC/blob/main/data"
class ASR_SECoMiCSC(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description="German dataset focusing on legal data",
features=datasets.Features({
"clip_id": datasets.Value("string"), # The ID of the clip
"path": datasets.Value("string"), # The path to the audio file
"audio": datasets.Audio(sampling_rate=16_000), # The audio file itself
"sentence": datasets.Value("string"), # The transcribed sentence
"split": datasets.Value("string") # The dataset split (Train/Test)
}),
supervised_keys=None,
)
def _split_generators(self, dl_manager):
# dl_manager.download_config.ignore_url_params = True
# Initialize paths and variables for training and test data
audio_path = {}
local_extracted_archive = {}
metadata_path = {}
split_type = {"train": datasets.Split.TRAIN, "test": datasets.Split.TEST}
# Iterates through the splits and loads or extracts the corresponding data
for split in split_type:
audio_path[split] = dl_manager.download(f"{_DATA_URL}/audio_{split}.tgz")
local_extracted_archive[split] = dl_manager.extract(audio_path[split]) if not dl_manager.is_streaming else None
metadata_path[split] = dl_manager.download_and_extract(f"{_DATA_URL}/metadata_{split}.csv.gz")
path_to_clips = "ASR_SECoMiCSC"
# Creates and returns the split generators
return [
datasets.SplitGenerator(
name=split_type[split],
gen_kwargs={
"local_extracted_archive": local_extracted_archive[split],
"audio_files": dl_manager.iter_archive(audio_path[split]),
"metadata_path": metadata_path[split],
"path_to_clips": path_to_clips,
"split": split # Pass the split name to _generate_examples
},
) for split in split_type
]
def _generate_examples(self, audio_files, metadata_path, path_to_clips, local_extracted_archive, split):
metadata = {}
# Open and read the metadata CSV file
with open(metadata_path, "r", encoding="utf-8") as f:
reader = csv.reader(f)
for row in reader:
filename, sentence = row
clip_id = filename.split('_')[0]
# Dynamically append the split name to path_to_clips
path = os.path.join(path_to_clips, split +"/wav/"+ clip_id, filename)
metadata[path] = {
"clip_id": clip_id,
"sentence": sentence,
"path": path,
}
id_ = 0
# Iterate through the audio files and create dataset entries
for path, file_content in audio_files:
if path in metadata:
result = dict(metadata[path])
path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path
audio_data = {"path": path, "bytes": file_content.read()}
result["audio"] = audio_data
result["path"] = path
yield id_, result
id_ += 1
else:
print(f"No metadata entry for {path}") |