Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code: ConfigNamesError
Exception: ReadTimeout
Message: (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 6feea33e-affd-4ecc-9bef-6ebf208cd027)')
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
config_names = get_dataset_config_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1031, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 996, in dataset_module_factory
return HubDatasetModuleFactory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 632, in get_module
data_files = DataFilesDict.from_patterns(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py", line 689, in from_patterns
else DataFilesList.from_patterns(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py", line 592, in from_patterns
origin_metadata = _get_origin_metadata(data_files, download_config=download_config)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py", line 506, in _get_origin_metadata
return thread_map(
File "/src/services/worker/.venv/lib/python3.9/site-packages/tqdm/contrib/concurrent.py", line 69, in thread_map
return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/tqdm/contrib/concurrent.py", line 51, in _executor_map
return list(tqdm_class(ex.map(fn, *iterables, chunksize=chunksize), **kwargs))
File "/src/services/worker/.venv/lib/python3.9/site-packages/tqdm/std.py", line 1169, in __iter__
for obj in iterable:
File "/usr/local/lib/python3.9/concurrent/futures/_base.py", line 609, in result_iterator
yield fs.pop().result()
File "/usr/local/lib/python3.9/concurrent/futures/_base.py", line 439, in result
return self.__get_result()
File "/usr/local/lib/python3.9/concurrent/futures/_base.py", line 391, in __get_result
raise self._exception
File "/usr/local/lib/python3.9/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py", line 485, in _get_single_origin_metadata
resolved_path = fs.resolve_path(data_file)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 198, in resolve_path
repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 125, in _repo_and_revision_exist
self._api.repo_info(
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_api.py", line 2816, in repo_info
return method(
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_api.py", line 2673, in dataset_info
r = get_session().get(path, headers=headers, timeout=timeout, params=params)
File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 602, in get
return self.request("GET", url, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 589, in request
resp = self.send(prep, **send_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 703, in send
r = adapter.send(request, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_http.py", line 96, in send
return super().send(request, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/adapters.py", line 635, in send
raise ReadTimeout(e, request=request)
requests.exceptions.ReadTimeout: (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 6feea33e-affd-4ecc-9bef-6ebf208cd027)')Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
WhisperKit Evals Dataset
Overview
The WhisperKit Evals Dataset is a comprehensive collection of our speech recognition evaluation results, specifically designed to benchmark the performance of WhisperKit models across various devices and operating systems. This dataset provides detailed insights into performance and quality metrics, and model behavior under different conditions.
Dataset Structure
The dataset is organized into JSON files, each representing a single evaluation run. The file naming convention encodes crucial metadata:
{Date}_{CommitHash}/{DeviceIdentifier}_{ModelVersion}_{Timestamp}_{Dataset}_{UUID}.json
File Content
Each JSON file contains an array of objects with a testInfo key, which includes:
diff: An array of character-level differences between the reference and predicted transcriptions.prediction: The full predicted transcription.reference: The full reference transcription.wer: Word Error Rate for the specific transcription.model: The model used for the test.device: The device on which the test was run.timings: Various timing metrics for the transcription process.datasetRepo: Repo on huggingface that was used as test data for the benchmarkdatasetDir: Subfolder in the datasetRepo containing the specific audio files usedaudioFile: The name of the audio file used.date: Date that the benchmark was performed
It also includes various system measurements taken during the benchmarking process such as system diagnostics, memory, latency, and configuration.
Key Features
- Comprehensive Model Evaluation: Results from various WhisperKit models, including different sizes and architectures.
- Cross-Device Performance: Tests run on a range of devices, from mobile to desktop, allowing for performance comparisons.
- Detailed Metrics: Includes Word Error Rate (WER), processing speed, and detailed transcription comparisons.
- Rich Metadata: Each file contains extensive metadata about the test conditions and setup.
Use Cases
This dataset is invaluable for:
- Benchmarking speech recognition models
- Analyzing performance across different hardware
- Identifying specific strengths and weaknesses in transcription tasks
Contributing
We welcome contributions to expand and improve this dataset. Please refer to BENCHMARKS.md in the source repo.
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