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import csv
import os
import urllib
import datasets
from datasets.utils.py_utils import size_str
import datasets
import requests
from datasets.utils.py_utils import size_str
from huggingface_hub import HfApi, HfFolder
# from .languages import LANGUAGES
#Used to get tar.gz file from mozilla website
from .release_stats import STATS
#Hard Links
_HOMEPAGE = "https://commonvoice.mozilla.org/en/datasets"
_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
_API_URL = "https://commonvoice.mozilla.org/api/v1"
class CommonVoiceConfig(datasets.BuilderConfig):
"""BuilderConfig for CommonVoice."""
def __init__(self, name, version, **kwargs):
self.language = "bn" # kwargs.pop("language", None)
self.release_date = "2022-04-27" # kwargs.pop("release_date", None)
self.num_clips = 231120 # kwargs.pop("num_clips", None)
self.num_speakers = 19863 # kwargs.pop("num_speakers", None)
self.validated_hr = 56.61 # kwargs.pop("validated_hr", None)
self.total_hr = 399.47 # kwargs.pop("total_hr", None)
self.size_bytes = 8262390506 # kwargs.pop("size_bytes", None)
self.size_human = size_str(self.size_bytes)
description = (
f"Common Voice speech to text dataset in {self.language} released on {self.release_date}. "
f"The dataset comprises {self.validated_hr} hours of validated transcribed speech data "
f"out of {self.total_hr} hours in total from {self.num_speakers} speakers. "
f"The dataset contains {self.num_clips} audio clips and has a size of {self.size_human}."
)
super(CommonVoiceConfig, self).__init__(
name=name,
version=datasets.Version(version),
description=description,
**kwargs,
)
class CommonVoice(datasets.GeneratorBasedBuilder):
#DEFAULT_CONFIG_NAME = "en"
DEFAULT_CONFIG_NAME = "bn"
DEFAULT_WRITER_BATCH_SIZE = 1000
BUILDER_CONFIGS = [
CommonVoiceConfig(
name="bn"#lang,
version= '9.0.0' #STATS["version"],
language= "Bengali" #LANGUAGES[lang],
release_date= "2022-04-27" #STATS["date"],
num_clips= 231120 #lang_stats["clips"],
num_speakers= 19863 #lang_stats["users"],
validated_hr= float(56.61) #float(lang_stats["validHrs"]),
total_hr= float(399.47) #float(lang_stats["totalHrs"]),
size_bytes= int(8262390506) #int(lang_stats["size"]),
)
#for lang, lang_stats in STATS["locales"].items()
]
def _info(self):
# total_languages = len(STATS["locales"])
# total_valid_hours = STATS["totalValidHrs"]
total_languages = 1 #len(STATS["locales"])
total_valid_hours = float(399.47) #STATS["totalValidHrs"]
description = (
"Common Voice Bangla is bengali AI's initiative to help teach machines how real people speak in Bangla. "
f"The dataset is for initial training of a general speech recognition model for Bangla."
)
features = datasets.Features(
{
"client_id": datasets.Value("string"),
"path": datasets.Value("string"),
"audio": datasets.features.Audio(sampling_rate=48_000),
"sentence": datasets.Value("string"),
"up_votes": datasets.Value("int64"),
"down_votes": datasets.Value("int64"),
"age": datasets.Value("string"),
"gender": datasets.Value("string"),
"accent": datasets.Value("string"),
"locale": 'bn',
"segment": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=description,
features=features,
supervised_keys=None,
# homepage=_HOMEPAGE,
license=_LICENSE,
# citation=_CITATION,
version=self.config.version,
#task_templates=[
# AutomaticSpeechRecognition(audio_file_path_column="path", transcription_column="sentence")
#],
)
def _get_bundle_url(self, locale, url_template):
# path = encodeURIComponent(path)
# path = url_template.replace("{locale}", locale)
path = url_template
path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'")
# use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024
# response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
response = requests.get(
f"{_API_URL}/bucket/dataset/{path}", timeout=10.0
).json()
return response["url"]
def _log_download(self, locale, bundle_version, auth_token):
if isinstance(auth_token, bool):
auth_token = HfFolder().get_token()
whoami = HfApi().whoami(auth_token)
email = whoami["email"] if "email" in whoami else ""
payload = {"email": email, "locale": locale, "dataset": bundle_version}
requests.post(f"{_API_URL}/{locale}/downloaders", json=payload).json()
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
hf_auth_token = dl_manager.download_config.use_auth_token
if hf_auth_token is None:
raise ConnectionError(
"Please set use_auth_token=True or use_auth_token='<TOKEN>' to download this dataset"
)
bundle_url_template = STATS["bundleURLTemplate"]
bundle_version = bundle_url_template.split("/")[0]
dl_manager.download_config.ignore_url_params = True
self._log_download(self.config.name, bundle_version, hf_auth_token)
archive_path = dl_manager.download(
self._get_bundle_url(self.config.name, bundle_url_template)
)
local_extracted_archive = (
dl_manager.extract(archive_path) if not dl_manager.is_streaming else None
)
if self.config.version < datasets.Version("5.0.0"):
path_to_data = ""
else:
path_to_data = "/".join([bundle_version, self.config.name])
path_to_clips = "/".join([path_to_data, "clips"]) if path_to_data else "clips"
#we provide our custom csvs with the huggingface repo so,
path_to_tsvs = "/" + "bengali_ai_tsv" + "/"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"local_extracted_archive": local_extracted_archive,
"archive_iterator": dl_manager.iter_archive(archive_path),
#"metadata_filepath": "/".join([path_to_data, "train.tsv"])
# if path_to_data
# else "train.tsv",
#custom train.tsv
"metadata_filepath": "/".join([path_to_data, "train.tsv"]) if path_to_data else "train.tsv",
"path_to_clips": path_to_clips,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"local_extracted_archive": local_extracted_archive,
"archive_iterator": dl_manager.iter_archive(archive_path),
"metadata_filepath": "/".join([path_to_data, "test.tsv"]) if path_to_data else "test.tsv",
"path_to_clips": path_to_clips,
"mode":"test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"local_extracted_archive": local_extracted_archive,
"archive_iterator": dl_manager.iter_archive(archive_path),
"metadata_filepath": "/".join([path_to_data, "dev.tsv"]) if path_to_data else "dev.tsv",
"path_to_clips": path_to_clips,
"mode":"dev",
},
),
# datasets.SplitGenerator(
# name=datasets.Split.TEST,
# gen_kwargs={
# "local_extracted_archive": local_extracted_archive,
# "archive_iterator": dl_manager.iter_archive(archive_path),
# #"metadata_filepath": "/".join([path_to_data, "test.tsv"])
# # if path_to_data
# # else "test.tsv",
# #custom test.tsv
# "metadata_filepath": "/".join([path_to_tsvs, "test.tsv"]),
# "path_to_clips": path_to_clips,
# },
# ),
# datasets.SplitGenerator(
# name=datasets.Split.VALIDATION,
# gen_kwargs={
# "local_extracted_archive": local_extracted_archive,
# "archive_iterator": dl_manager.iter_archive(archive_path),
# # "metadata_filepath": "/".join([path_to_data, "dev.tsv"])
# # if path_to_data
# # else "dev.tsv",
# #custom test.tsv
# "metadata_filepath": "/".join([path_to_tsvs, "dev.tsv"]),
# "path_to_clips": path_to_clips,
# },
# ),
]
def _generate_examples(
self,
local_extracted_archive,
archive_iterator,
metadata_filepath,
path_to_clips,
):
"""Yields examples."""
data_fields = list(self._info().features.keys())
metadata = {}
metadata_found = True
with open(metadata_filepath, "rb") as file_obj:
lines = (line.decode("utf-8") for line in file_obj)
#lines = (line.decode("utf-8") for line in f)
reader = csv.DictReader(lines, delimiter="\t", quoting=csv.QUOTE_NONE)
for row in reader:
# set absolute path for mp3 audio file
if not row["path"].endswith(".mp3"):
row["path"] += ".mp3"
row["path"] = os.path.join(path_to_clips, row["path"])
# accent -> accents in CV 8.0
if "accents" in row:
row["accent"] = row["accents"]
del row["accents"]
# if data is incomplete, fill with empty values
for field in data_fields:
if field not in row:
row[field] = ""
metadata[row["path"]] = row
for path, f in archive_iterator:
if path.startswith(path_to_clips):
assert metadata_found, "Found audio clips before the metadata TSV file."
if not metadata:
break
if path in metadata:
result = metadata[path]
# set the audio feature and the path to the extracted file
path = (
os.path.join(local_extracted_archive, path)
if local_extracted_archive
else path
)
result["audio"] = {"path": path, "bytes": f.read()}
# set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
result["path"] = path if local_extracted_archive else None
yield path, result
# def _generate_examples(
# self,
# local_extracted_archive,
# archive_iterator,
# metadata_filepath,
# path_to_clips,
# ):
# """Yields examples."""
# data_fields = list(self._info().features.keys())
# metadata = {}
# metadata_found = False
# for path, f in archive_iterator:
# if path == metadata_filepath:
# metadata_found = True
# lines = (line.decode("utf-8") for line in f)
# reader = csv.DictReader(lines, delimiter="\t", quoting=csv.QUOTE_NONE)
# for row in reader:
# # set absolute path for mp3 audio file
# if not row["path"].endswith(".mp3"):
# row["path"] += ".mp3"
# row["path"] = os.path.join(path_to_clips, row["path"])
# # accent -> accents in CV 8.0
# if "accents" in row:
# row["accent"] = row["accents"]
# del row["accents"]
# # if data is incomplete, fill with empty values
# for field in data_fields:
# if field not in row:
# row[field] = ""
# metadata[row["path"]] = row
# elif path.startswith(path_to_clips):
# assert metadata_found, "Found audio clips before the metadata TSV file."
# if not metadata:
# break
# if path in metadata:
# result = metadata[path]
# # set the audio feature and the path to the extracted file
# path = (
# os.path.join(local_extracted_archive, path)
# if local_extracted_archive
# else path
# )
# result["audio"] = {"path": path, "bytes": f.read()}
# # set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
# result["path"] = path if local_extracted_archive else None
# yield path, result
# 'bn': {'duration': 1438112808, 'reportedSentences': 693, 'buckets': {'dev': 7748, 'invalidated': 5844, 'other': 192522,
# 'reported': 717, 'test': 7748, 'train': 14503, 'validated': 32754}, 'clips': 231120, 'splits': {'accent': {'': 1},
# 'age': {'thirties': 0.02, 'twenties': 0.22, '': 0.72, 'teens': 0.04, 'fourties': 0},
# 'gender': {'male': 0.24, '': 0.72, 'female': 0.04, 'other': 0}}, 'users': 19863, 'size': 8262390506,
# 'checksum': '599a5f7c9e55a297928da390345a19180b279a1f013081e7255a657fc99f98d5', 'avgDurationSecs': 6.222,
# 'validDurationSecs': 203807.316, 'totalHrs': 399.47, 'validHrs': 56.61},
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