|
"""Tamil Dataset - contains 90 mins of speech data""" |
|
|
|
import csv |
|
import json |
|
import os |
|
|
|
import datasets |
|
|
|
_CITATION = """\ |
|
@misc{simpledata_1, |
|
title = {Whisper model for tamil-to-eng translation}, |
|
publisher = {Achitha}, |
|
year = {2022}, |
|
} |
|
@misc{simpledata_2, |
|
title = {Fine-tuning whisper model}, |
|
publisher = {Achitha}, |
|
year = {2022}, |
|
} |
|
""" |
|
_DESCRIPTION = """\ |
|
The data contains roughly one and half hours of audio and trasncripts in Tamil language. |
|
""" |
|
|
|
_HOMEPAGE = "" |
|
|
|
_LICENSE = "" |
|
|
|
|
|
_METADATA_URLS = { |
|
"train": "data/train.jsonl", |
|
"test": "data/test.jsonl" |
|
} |
|
_URLS = { |
|
"train": "data/train.tar.gz", |
|
"test": "data/test.tar.gz", |
|
|
|
} |
|
|
|
class simple_data(datasets.GeneratorBasedBuilder): |
|
|
|
|
|
VERSION = datasets.Version("1.1.0") |
|
def _info(self): |
|
features = datasets.Features( |
|
{ |
|
"audio": datasets.Audio(sampling_rate=16_000), |
|
"path": datasets.Value("string"), |
|
"sentence": datasets.Value("string"), |
|
|
|
} |
|
) |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=features, |
|
supervised_keys=("sentence", "label"), |
|
homepage=_HOMEPAGE, |
|
license=_LICENSE, |
|
citation=_CITATION, |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
metadata_paths = dl_manager.download(_METADATA_URLS) |
|
train_archive = dl_manager.download(_URLS["train"]) |
|
test_archive = dl_manager.download(_URLS["test"]) |
|
local_extracted_train_archive = dl_manager.extract(train_archive) if not dl_manager.is_streaming else None |
|
local_extracted_test_archive = dl_manager.extract(test_archive) if not dl_manager.is_streaming else None |
|
test_archive = dl_manager.download(_URLS["test"]) |
|
train_dir = "train" |
|
test_dir = "test" |
|
|
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"metadata_path": metadata_paths["train"], |
|
"local_extracted_archive": local_extracted_train_archive, |
|
"path_to_clips": train_dir, |
|
"audio_files": dl_manager.iter_archive(train_archive), |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TEST, |
|
gen_kwargs={ |
|
"metadata_path": metadata_paths["test"], |
|
"local_extracted_archive": local_extracted_test_archive, |
|
"path_to_clips": test_dir, |
|
"audio_files": dl_manager.iter_archive(test_archive), |
|
}, |
|
), |
|
|
|
] |
|
|
|
def _generate_examples(self, metadata_path, local_extracted_archive, path_to_clips, audio_files): |
|
"""Yields examples as (key, example) tuples.""" |
|
examples = {} |
|
with open(metadata_path, encoding="utf-8") as f: |
|
for key, row in enumerate(f): |
|
data = json.loads(row) |
|
examples[data["path"]] = data |
|
inside_clips_dir = False |
|
id_ = 0 |
|
for path, f in audio_files: |
|
if path.startswith(path_to_clips): |
|
inside_clips_dir = True |
|
if path in examples: |
|
result = examples[path] |
|
path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path |
|
result["audio"] = {"path": path, "bytes": f.read()} |
|
result["path"] = path |
|
yield id_, result |
|
id_ += 1 |
|
elif inside_clips_dir: |
|
break |
|
|