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import csv |
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import json |
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import os |
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from pathlib import Path |
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from typing import Dict, List, Tuple |
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import datasets |
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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import Licenses, Tasks |
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_CITATION = """\ |
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@inproceedings{commonvoice:2020, |
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author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, |
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title = {Common Voice: A Massively-Multilingual Speech Corpus}, |
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booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, |
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pages = {4211--4215}, |
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year = 2020 |
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} |
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""" |
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_DATASETNAME = "commonvoice_120" |
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_DESCRIPTION = """\ |
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The Common Mozilla Voice dataset consists of a unique MP3 and corresponding text file. |
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Many of the 26119 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. |
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The dataset currently consists of 17127 validated hours in 104 languages, but more voices and languages are always added. |
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Before using this dataloader, please accept the acknowledgement at https://huggingface.co/datasets/mozilla-foundation/common_voice_12_0 and use huggingface-cli login for authentication |
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""" |
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_HOMEPAGE = "https://commonvoice.mozilla.org/en/datasets" |
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_LANGUAGES = ["cnh", "ind", "tha", "vie"] |
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_LANG_TO_CVLANG = {"cnh": "cnh", "ind": "id", "tha": "th", "vie": "vi"} |
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_AGE_TO_INT = {"": None, "teens": 10, "twenties": 20, "thirties": 30, "fourties": 40, "fifties": 50, "sixties": 60, "seventies": 70, "eighties": 80} |
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_LICENSE = Licenses.CC0_1_0.value |
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_LOCAL = False |
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_COMMONVOICE_URL_TEMPLATE = "https://huggingface.co/datasets/mozilla-foundation/common_voice_12_0/resolve/main/" |
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_URLS = {"audio": _COMMONVOICE_URL_TEMPLATE + "audio/{lang}/{split}/{lang}_{split}_{shard_idx}.tar", "transcript": _COMMONVOICE_URL_TEMPLATE + "transcript/{lang}/{split}.tsv", "n_shards": _COMMONVOICE_URL_TEMPLATE + "n_shards.json"} |
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_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION, Tasks.TEXT_TO_SPEECH] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class Commonvoice120(datasets.GeneratorBasedBuilder): |
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"""This is the dataloader for CommonVoice 12.0 Mozilla""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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BUILDER_CONFIGS = ( |
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*[ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_{lang}{'_' if lang else ''}source", |
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version=datasets.Version(_SOURCE_VERSION), |
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description=f"{_DATASETNAME} source schema for {lang}", |
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schema="source", |
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subset_id=f"{_DATASETNAME}{'_' if lang else ''}{lang}", |
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) |
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for lang in ["", *_LANGUAGES] |
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], |
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*[ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_{lang}{'_' if lang else ''}seacrowd_sptext", |
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version=datasets.Version(_SEACROWD_VERSION), |
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description=f"{_DATASETNAME} SEACrowd schema for {lang}", |
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schema="seacrowd_sptext", |
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subset_id=f"{_DATASETNAME}{'_' if lang else ''}{lang}", |
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) |
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for lang in ["", *_LANGUAGES] |
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], |
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) |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"client_id": datasets.Value("string"), |
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"path": datasets.Value("string"), |
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"audio": datasets.features.Audio(sampling_rate=48_000), |
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"sentence": datasets.Value("string"), |
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"up_votes": datasets.Value("int64"), |
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"down_votes": datasets.Value("int64"), |
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"age": datasets.Value("string"), |
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"gender": datasets.Value("string"), |
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"accent": datasets.Value("string"), |
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"locale": datasets.Value("string"), |
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"segment": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == "seacrowd_sptext": |
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features = schemas.speech_text_features |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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lang_code = self.config.subset_id.split("_")[-1] |
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languages = [_LANG_TO_CVLANG.get(lang, lang) for lang in (_LANGUAGES if lang_code == "120" else [lang_code])] |
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n_shards_path = dl_manager.download_and_extract(_URLS["n_shards"]) |
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with open(n_shards_path, encoding="utf-8") as f: |
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n_shards = json.load(f) |
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audio_urls = {} |
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meta_urls = {} |
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splits = ("train", "dev", "test") |
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for split in splits: |
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audio_urls[split] = [_URLS["audio"].format(lang=lang, split=split, shard_idx=i) for lang in languages for i in range(n_shards[lang][split])] |
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meta_urls[split] = [_URLS["transcript"].format(lang=lang, split=split) for lang in languages] |
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archive_paths = dl_manager.download(audio_urls) |
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local_extracted_archive_paths = dl_manager.extract(archive_paths) |
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meta_paths = dl_manager.download_and_extract(meta_urls) |
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split_names = { |
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"train": datasets.Split.TRAIN, |
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"dev": datasets.Split.VALIDATION, |
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"test": datasets.Split.TEST, |
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} |
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return [ |
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datasets.SplitGenerator( |
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name=split_names.get(split, split), |
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gen_kwargs={ |
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"local_extracted_archive_paths": local_extracted_archive_paths.get(split), |
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"audio_archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)], |
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"meta_paths": meta_paths[split], |
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"split": "train", |
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}, |
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) |
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for split in splits |
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] |
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def _generate_examples(self, local_extracted_archive_paths: [Path], audio_archives: [Path], meta_paths: [Path], split: str) -> Tuple[int, Dict]: |
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data_fields = list(self._info().features.keys()) |
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metadata = {} |
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for meta_path in meta_paths: |
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with open(meta_path, encoding="utf-8") as f: |
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reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) |
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for row in reader: |
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if not row["path"].endswith(".mp3"): |
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row["path"] += ".mp3" |
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if "accents" in row: |
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row["accent"] = row["accents"] |
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del row["accents"] |
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for field in data_fields: |
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if field not in row: |
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row[field] = "" |
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metadata[row["path"]] = row |
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if self.config.schema == "source": |
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for i, audio_archive in enumerate(audio_archives): |
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for path, file in audio_archive: |
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_, filename = os.path.split(path) |
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if filename in metadata: |
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src_result = dict(metadata[filename]) |
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path = os.path.join(local_extracted_archive_paths[i], path) |
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result = { |
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"client_id": src_result["client_id"], |
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"path": path, |
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"audio": {"path": path, "bytes": file.read()}, |
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"sentence": src_result["sentence"], |
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"up_votes": src_result["up_votes"], |
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"down_votes": src_result["down_votes"], |
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"age": src_result["age"], |
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"gender": src_result["gender"], |
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"accent": src_result["accent"], |
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"locale": src_result["locale"], |
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"segment": src_result["segment"], |
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} |
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yield path, result |
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elif self.config.schema == "seacrowd_sptext": |
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for i, audio_archive in enumerate(audio_archives): |
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for path, file in audio_archive: |
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_, filename = os.path.split(path) |
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if filename in metadata: |
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src_result = dict(metadata[filename]) |
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path = os.path.join(local_extracted_archive_paths[i], path) |
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result = { |
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"id": src_result["path"].replace(".mp3", ""), |
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"path": path, |
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"audio": {"path": path, "bytes": file.read()}, |
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"text": src_result["sentence"], |
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"speaker_id": src_result["client_id"], |
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"metadata": { |
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"speaker_age": _AGE_TO_INT[src_result["age"]], |
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"speaker_gender": src_result["gender"], |
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}, |
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} |
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yield path, result |
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