diff --git a/.README.md.swp b/.README.md.swp new file mode 100644 index 0000000000000000000000000000000000000000..c9f1652552c4d932826d17da3f53cc385f04a6b8 Binary files /dev/null and b/.README.md.swp differ diff --git a/README.md b/README.md index e4bbaa4b128318634d583f99ebd17743af066940..65c2ad0830d625e984435d8bd994935c3f13b89d 100644 --- a/README.md +++ b/README.md @@ -24,6 +24,11 @@ language: pretty_name: Simple Voice Questions size_categories: - 100K 1 and data.shape[1] > 1: - raise ValueError("Only mono WAV files are supported.") - - # Convert data to float and normalize - if data.dtype == np.int16: - x = data.astype(np.float32) / np.iinfo(np.int16).max - elif data.dtype == np.int32: - x = data.astype(np.float32) / np.iinfo(np.int32).max - elif data.dtype == np.float32: - x = data - else: - raise TypeError(f"Unsupported data type: {data.dtype}") - if resample_hz is not None and resample_hz != rate: - x = librosa.resample(x, orig_sr=rate, target_sr=resample_hz) - return x, rate - - -def read_utt_index(basepath): - """Read utt_index.jsonl file to a dict of {uttid: path:index}.""" - df = pd.read_json(os.path.join(basepath, "utt_index.jsonl"), lines=True) - return dict(zip(df["utt_id"], df["index"])) - - -class UttLookup: - """Lookup utterances by utt_id with optional resampling. - - Usage: - utt_lookup = UttLookup(basepath) - waveform = utt_lookup(utt_id) - """ - - def __init__(self, basepath, resample_hz: float | None = None): - self.basepath = basepath - self.resample_hz = resample_hz - self.utt_id_to_path_idx = read_utt_index(basepath) - self.readers = {} - self.orig_sample_rate_ = None - - @property - def orig_sample_rate(self): - if self.orig_sample_rate_ is None: - utt_id = next(iter(self.utt_id_to_path_idx)) - self(utt_id) - return self.orig_sample_rate_ - - def __call__(self, utt_id: str): - path, idx = self.utt_id_to_path_idx[utt_id].split(":") - if path not in self.readers: - array_record_path = os.path.join(self.basepath, f"{path}.array_record") - self.readers[path] = array_record.ArrayRecordReader( - array_record_path - ) - b = self.readers[path].read([int(idx)]) - waveform, sample_rate = read_wav_bytes_to_normalized_float( - b[0], resample_hz=self.resample_hz - ) - if self.orig_sample_rate_ is None: - self.orig_sample_rate_ = sample_rate - if sample_rate != self.orig_sample_rate_: - raise ValueError( - f"Sample rate mismatch: {sample_rate} != {self.orig_sample_rate_}" - ) - return waveform - - -def generate_examples(filepath, resample_hz: float | None = None): - """Generate examples from a jsonl task file.""" - basepath = os.path.dirname(filepath) - utt_lookup = UttLookup(basepath, resample_hz=resample_hz) - task = pd.read_json(filepath, lines=True) - for ex in task.to_dict(orient="records"): - utt = utt_lookup(ex["utt_id"]) - ex["waveform"] = utt - yield ex - - -_CITATION = """\ -@InProceedings{mseb, -title = {Massive Sound Embedding Benchmark (MSEB)}, -author={Georg Heigold, Ehsan Variani, Tom Bagby, Ji Ma, Cyril Allauzen, Shankar Kumar, Michael Riley} -year={2025} -} -""" - -_NUM_SHARDS = 128 # Internal sharding for parallel data loading. - - -class SvqDataset(datasets.GeneratorBasedBuilder): - """SVQ dataset.""" - - VERSION = datasets.Version("1.1.0") - - BUILDER_CONFIGS = [ - datasets.BuilderConfig(name=name, description=desc) - for name, desc in [ - ("span_reasoning_in_lang", "Span reasoning in language."), - ("span_retrieval_in_lang", "Span retrieval in language."), - ("span_reasoning_cross_lang", "Span reasoning cross language."), - ("span_retrieval_cross_lang", "Span retrieval cross language."), - ("passage_retrieval_in_lang", "Passage retrieval in language."), - ("passage_retrieval_cross_lang", "Passage retrieval cross language."), - ("document_retrieval_in_lang", "Document retrieval in language."), - ( - "document_retrieval_cross_lang", - "Document retrieval cross language.", - ), - ] - ] - - DEFAULT_WRITER_BATCH_SIZE = 64 - - def _info(self): - task = self.config.name - features = { - "utt_id": datasets.Value("string"), - "waveform": datasets.Sequence(datasets.Value("float32")), - "text": datasets.Value("string"), - "locale": datasets.Value("string"), - "environment": datasets.Value("string"), - "speaker_id": datasets.Value("string"), - "speaker_age": datasets.Value("int32"), - "speaker_gender": datasets.Value("string"), - "page_id": datasets.Value("string"), - "page_title": datasets.Value("string"), - "passage_id": datasets.Value("string"), - "passage_text": datasets.Value("string"), - } - if "span" in task: - features["span"] = datasets.Value("string") - return datasets.DatasetInfo( - description=( - "Simple Voice Queries (SVQ) dataset, Task: span reasoning in" - " language." - ), - features=datasets.Features(**features), - homepage="https://huggingface.co/datasets/google/svq", - license="Apache 2.0", - citation=_CITATION, - ) - - def _split_generators(self, dl_manager): - basepath = os.getcwd() - task = self.config.name - return [ - datasets.SplitGenerator( - name="eval", - gen_kwargs={ - "filepath": os.path.join( - basepath, f"{task}.jsonl" - ), - "shards": list(range(_NUM_SHARDS)), - "resample_hz": 16000, - "task_name": task, - }, - ), - ] - - def _generate_examples( - self, filepath=None, shards=None, resample_hz=None, task_name=None - ): - basepath = os.path.dirname(filepath) - utt_lookup = UttLookup(basepath, resample_hz=resample_hz) - task = pd.read_json(filepath, lines=True) - task = np.array_split(task, _NUM_SHARDS) - task_shards = [task[idx].to_dict(orient="records") for idx in shards] - del task - for shard in task_shards: - for ex in shard: - utt = utt_lookup(ex["utt_id"]) - ex["waveform"] = utt - del ex["task"] - if "span" not in task_name: - del ex["span"] - yield "_".join([ex["utt_id"], ex["passage_id"]]), ex