Datasets:

ytdata
/

vitw-context-select

---
# Upstream dataset card states no license; verify upstream terms before making this public.
license: unknown
language:
- en
task_categories:
- automatic-speech-recognition
pretty_name: VITW Context-Select (EN)
tags:
- speech
- asr
- context
- robustness
configs:
- config_name: strict
  default: true
  data_files:
  - split: validation
    path: data/strict/validation-*.parquet
  - split: test
    path: data/strict/test-*.parquet
- config_name: prefix
  data_files:
  - split: validation
    path: data/prefix/validation-*.parquet
  - split: test
    path: data/prefix/test-*.parquet
---
# VITW Context-Select (EN)

A **selected subset of the original English audio** of
[`zhifeixie/Voices-in-the-Wild-Bench`](https://huggingface.co/datasets/zhifeixie/Voices-in-the-Wild-Bench)
(revision `788f5d72c6b0e9091b5c2e432370923b6f9f0660`). **No noise or other processing was added**;
audio bytes are the upstream WAV files (24 kHz mono PCM16), SHA-256 checked.

Purpose: test whether an ASR system (e.g. an MoE-fusion ASR with an LLM branch) uses **preceding
sentence context** to recover words that are **acoustically hard to recognise**.

## Configs

| config | rule (per target word; an utterance is kept if ≥1 word qualifies) | validation | test |
|---|---|---|---|
| `strict` (default) | acoustically ambiguous vs a near-sounding word **and** the disambiguating context is in the prefix **and** Whisper large-v3 (no prompt) actually misrecognised the word (number-format / spelling variants such as 7/seven, centre/center are not counted as errors) | 85 utts / 0.20 h / 84 groups | 372 utts / 0.90 h / 365 groups |
| `prefix` | acoustically ambiguous **and** context in the prefix (no Whisper-error requirement) | 283 utts / 0.65 h / 266 groups | 1,076 utts / 2.61 h / 990 groups |

`validation` = dev partition (used for threshold design); `test` = held-out partition. Splits are made at
**template-group** level (near-duplicate sentences never cross splits), seed 20261108.

```python
from datasets import load_dataset
test = load_dataset("ytdata/vitw-context-select", "strict", split="test")
# only the test files:
test = load_dataset("ytdata/vitw-context-select", data_files={"test": "data/strict/test-*.parquet"}, split="test")
```

## How words were selected (machine screening, no human listening yet)

1. **Candidates (text only):** every reference word with a homophone / near-phone alternative from CMUdict
   (e.g. *leak / league*, *seats / seeds*), alternatives with wordfreq zipf ≥ 2.5. Function words excluded.
2. **Acoustic ambiguity:** Whisper large-v3 forced-choice on a ±0.3 s crop around the word; ambiguous if the
   best near-sounding alternative is within e^1 of the target, or Whisper's free decoding got the word wrong.
   Exact homophones are not counted as acoustic evidence.
3. **Context:** Qwen2.5-7B-Instruct scores the reference word vs alternatives given the **prefix**
   (and full sentence); kept when the reference is ≥10× more likely than the hardest alternative and the
   context adds ≥e^1 beyond the word's prior.

Rule file and all scores are in the build repo (`configs/selection_rules.yaml`, selection v1).

## Columns

`audio`, `sample_id`, `name`, `file_name`, `original_split` (`real_*`/`syn_*` condition), `source_type`,
`condition`, `mixed_components`, `reference_raw`, `reference_normalized`, `group_id`, `partition`,
`duration_sec`, `audio_sha256`, `n_targets`, `selector_hypothesis` (Whisper large-v3 greedy output), and
`targets`: JSON list, one entry per qualifying word with `target_text`, `target_char_span` /
`target_word_span` (half-open), `target_time_sec` (approximate, from Whisper word timestamps, unverified),
`alternatives`, `selector_target_word`, `crop_margin`, `context_availability`, `prefix_margin`, `prefix_shift`,
`full_margin`, `hardest_alternative_prefix`.

## Limitations

- Labels are **provisional (machine-screened)**; nobody has listened to the audio yet.
- `strict` is enriched for errors of one specific model (Whisper large-v3); it is "hard for that selector",
  not representative of all ASR or of the full benchmark. Report full-benchmark results separately.
- Whisper has an internal LM, so "acoustic ambiguity" is a proxy, not ground truth.
- Text overlap was checked only against the Voices-in-the-Wild-2M English text manifest (no exact or
  near matches found); overlap with other training data is unknown.
- Source sentences are short, highly predictable everyday sentences; low-context counterexamples are rare.
- One truncated upstream clip (`vitw_04834_real_en_far_field`, 0.08 s) is excluded.

## 中文摘要

從 Voices-in-the-Wild-Bench 英文原始音檔（**未加任何噪音**）挑出「目標詞聽起來易混淆、但前文上下文可判斷」
的句子。`strict` 另要求 Whisper large-v3 實際聽錯該詞（test 372 句）；`prefix` 較寬（test 1,076 句）。
validation 為 dev（已用於門檻設計），結論請以 test 報告。標籤為機器初篩，尚無人工聽音驗證。

	---
	# Upstream dataset card states no license; verify upstream terms before making this public.
	license: unknown
	language:
	- en
	task_categories:
	- automatic-speech-recognition
	pretty_name: VITW Context-Select (EN)
	tags:
	- speech
	- asr
	- context
	- robustness
	configs:
	- config_name: strict
	default: true
	data_files:
	- split: validation
	path: data/strict/validation-*.parquet
	- split: test
	path: data/strict/test-*.parquet
	- config_name: prefix
	data_files:
	- split: validation
	path: data/prefix/validation-*.parquet
	- split: test
	path: data/prefix/test-*.parquet
	---

	# VITW Context-Select (EN)

	A selected subset of the original English audio of
	[`zhifeixie/Voices-in-the-Wild-Bench`](https://huggingface.co/datasets/zhifeixie/Voices-in-the-Wild-Bench)
	(revision `788f5d72c6b0e9091b5c2e432370923b6f9f0660`). No noise or other processing was added;
	audio bytes are the upstream WAV files (24 kHz mono PCM16), SHA-256 checked.

	Purpose: test whether an ASR system (e.g. an MoE-fusion ASR with an LLM branch) uses **preceding
	sentence context to recover words that are acoustically hard to recognise**.

	## Configs

	\| config \| rule (per target word; an utterance is kept if ≥1 word qualifies) \| validation \| test \|
	\|---\|---\|---\|---\|
	\| `strict` (default) \| acoustically ambiguous vs a near-sounding word and the disambiguating context is in the prefix and Whisper large-v3 (no prompt) actually misrecognised the word (number-format / spelling variants such as 7/seven, centre/center are not counted as errors) \| 85 utts / 0.20 h / 84 groups \| 372 utts / 0.90 h / 365 groups \|
	\| `prefix` \| acoustically ambiguous and context in the prefix (no Whisper-error requirement) \| 283 utts / 0.65 h / 266 groups \| 1,076 utts / 2.61 h / 990 groups \|

	`validation` = dev partition (used for threshold design); `test` = held-out partition. Splits are made at
	template-group level (near-duplicate sentences never cross splits), seed 20261108.

	```python
	from datasets import load_dataset
	test = load_dataset("ytdata/vitw-context-select", "strict", split="test")
	# only the test files:
	test = load_dataset("ytdata/vitw-context-select", data_files={"test": "data/strict/test-*.parquet"}, split="test")
	```

	## How words were selected (machine screening, no human listening yet)

	1. Candidates (text only): every reference word with a homophone / near-phone alternative from CMUdict
	(e.g. leak / league, seats / seeds), alternatives with wordfreq zipf ≥ 2.5. Function words excluded.
	2. Acoustic ambiguity: Whisper large-v3 forced-choice on a ±0.3 s crop around the word; ambiguous if the
	best near-sounding alternative is within e^1 of the target, or Whisper's free decoding got the word wrong.
	Exact homophones are not counted as acoustic evidence.
	3. Context: Qwen2.5-7B-Instruct scores the reference word vs alternatives given the prefix
	(and full sentence); kept when the reference is ≥10× more likely than the hardest alternative and the
	context adds ≥e^1 beyond the word's prior.

	Rule file and all scores are in the build repo (`configs/selection_rules.yaml`, selection v1).

	## Columns

	`audio`, `sample_id`, `name`, `file_name`, `original_split` (`real_`/`syn_` condition), `source_type`,
	`condition`, `mixed_components`, `reference_raw`, `reference_normalized`, `group_id`, `partition`,
	`duration_sec`, `audio_sha256`, `n_targets`, `selector_hypothesis` (Whisper large-v3 greedy output), and
	`targets`: JSON list, one entry per qualifying word with `target_text`, `target_char_span` /
	`target_word_span` (half-open), `target_time_sec` (approximate, from Whisper word timestamps, unverified),
	`alternatives`, `selector_target_word`, `crop_margin`, `context_availability`, `prefix_margin`, `prefix_shift`,
	`full_margin`, `hardest_alternative_prefix`.

	## Limitations

	- Labels are provisional (machine-screened); nobody has listened to the audio yet.
	- `strict` is enriched for errors of one specific model (Whisper large-v3); it is "hard for that selector",
	not representative of all ASR or of the full benchmark. Report full-benchmark results separately.
	- Whisper has an internal LM, so "acoustic ambiguity" is a proxy, not ground truth.
	- Text overlap was checked only against the Voices-in-the-Wild-2M English text manifest (no exact or
	near matches found); overlap with other training data is unknown.
	- Source sentences are short, highly predictable everyday sentences; low-context counterexamples are rare.
	- One truncated upstream clip (`vitw_04834_real_en_far_field`, 0.08 s) is excluded.

	## 中文摘要

	從 Voices-in-the-Wild-Bench 英文原始音檔（未加任何噪音）挑出「目標詞聽起來易混淆、但前文上下文可判斷」
	的句子。`strict` 另要求 Whisper large-v3 實際聽錯該詞（test 372 句）；`prefix` 較寬（test 1,076 句）。
	validation 為 dev（已用於門檻設計），結論請以 test 報告。標籤為機器初篩，尚無人工聽音驗證。