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- test/audio/segment_kmr_0_01477.flac +3 -0
- test/audio/segment_kmr_ckb_1_00330.flac +3 -0
- test/audio/segment_kmr_ckb_1_00418.flac +3 -0
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- test/metadata.jsonl +0 -0
- train/metadata.jsonl +0 -0
- validation/metadata.jsonl +0 -0
- validation_report.json +27 -0
README.md
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| 1 |
+
---
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| 2 |
+
license: cc-by-4.0
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| 3 |
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pretty_name: Mixed-Language Speech Detection Pilot
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| 4 |
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task_categories:
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| 5 |
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- audio-classification
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| 6 |
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language:
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| 7 |
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- tr
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| 8 |
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- kmr
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| 9 |
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- ckb
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| 10 |
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- ar
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| 11 |
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- fa
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| 12 |
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- en
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| 13 |
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size_categories:
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| 14 |
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- 1K<n<10K
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| 15 |
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configs:
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- config_name: default
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| 17 |
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drop_labels: true
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| 18 |
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data_files:
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| 19 |
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- split: train
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| 20 |
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path: train/**
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| 21 |
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- split: validation
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| 22 |
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path: validation/**
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| 23 |
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- split: test
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| 24 |
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path: test/**
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| 25 |
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---
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| 26 |
+
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| 27 |
+
# Mixed-Language Speech Detection Pilot
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| 28 |
+
|
| 29 |
+
This dataset is a 6,000-clip binary audio-classification pilot for detecting
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| 30 |
+
whether an utterance contains one language (`label = 0`) or more than one
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| 31 |
+
language (`label = 1`). It covers Turkish (`tur`), Northern Kurdish/Kurmanji
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| 32 |
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(`kmr`), Central Kurdish/Sorani (`ckb`), Arabic (`ara`), Persian (`fas`), and
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| 33 |
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English (`eng`).
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| 34 |
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|
| 35 |
+
## Dataset composition
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| 36 |
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| 37 |
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| Construction | Mixed | Monolingual | Total |
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| 38 |
+
|---|---:|---:|---:|
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| 39 |
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| Single-call OmniVoice | 500 | 500 | 1,000 |
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| 40 |
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| Segment-level OmniVoice | 1,500 | 1,500 | 3,000 |
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| 41 |
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| Real-speech concatenation | 1,000 | 1,000 | 2,000 |
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| 42 |
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| **Total** | **3,000** | **3,000** | **6,000** |
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| 43 |
+
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| 44 |
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The split is 80/10/10: 4,800 train, 600 validation, and 600 test clips. Cloned
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| 45 |
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voice references, source recordings, and text pools are isolated by split.
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| 46 |
+
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| 47 |
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## Audio
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| 48 |
+
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| 49 |
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- 16 kHz mono FLAC
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| 50 |
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- 60% clean
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| 51 |
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- 20% synthetic colored background noise
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| 52 |
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- 10% telephone/radio filtering
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| 53 |
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- 10% compression or short-room reverberation
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| 54 |
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|
| 55 |
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Augmentation is applied after complete clip assembly and has the same
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| 56 |
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distribution for both labels.
|
| 57 |
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|
| 58 |
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## Synthetic speech
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| 59 |
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|
| 60 |
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Synthetic clips were generated with `k2-fsa/OmniVoice`. Twenty distinct
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| 61 |
+
synthetic reference voices were first created with OmniVoice voice design, then
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| 62 |
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used through the voice-cloning interface. Twelve references are exclusive to
|
| 63 |
+
train, four to validation, and four to test. No real person's voice was cloned.
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| 64 |
+
|
| 65 |
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For segment-level mixed clips, 80% use one cloned voice across all languages and
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| 66 |
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20% alternate between two cloned voices. Switch patterns are approximately 70%
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| 67 |
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A→B, 25% A→B→A, and 5% A→B→A→B. Segment boundaries in `segments` reflect the
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| 68 |
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assembled audio before length-preserving full-clip augmentation.
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| 69 |
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|
| 70 |
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Single-call mixed generation uses language-agnostic conditioning and should be
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| 71 |
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treated as the experimental subset described by `construction =
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| 72 |
+
"single_call_tts"`. It is intended primarily for clip-level evaluation.
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| 73 |
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| 74 |
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## Real speech
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| 75 |
+
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| 76 |
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Real concatenations use recordings from:
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| 77 |
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| 78 |
+
- Google FLEURS (`google/fleurs`), CC BY 4.0
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| 79 |
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- KMR-Tatoeba (`aranemini/kmr-tatoeba`), CC BY 4.0
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| 80 |
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- LibriSpeech `dev-clean`, CC BY 4.0
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| 81 |
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| 82 |
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Source recording IDs and corpora are retained in each real sample. No source
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| 83 |
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recording is used more than three times or shared across dataset splits. Half of
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| 84 |
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the real joins are same-corpus and half different-corpus, independently for
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| 85 |
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mixed and monolingual labels.
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| 86 |
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| 87 |
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## Fields
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| 88 |
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| 89 |
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- `audio`: viewer-decoded audio from `file_name`
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| 90 |
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- `id`: stable sample identifier
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| 91 |
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- `label`: `0` for monolingual, `1` for mixed-language
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| 92 |
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- `construction`: `single_call_tts`, `segment_tts`, or `real_concatenation`
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| 93 |
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- `languages`: unique ISO 639-3 language codes present
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| 94 |
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- `language_sequence`: ordered span languages
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| 95 |
+
- `speaker_condition`: same or different voice condition
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| 96 |
+
- `segments`: start/end time, language, text, and provenance per span; null for
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| 97 |
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single-call generation
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| 98 |
+
- `text`: complete input text for single-call generation; null otherwise
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| 99 |
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- `audio_condition` and `augmentation_detail`: full-clip audio treatment
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| 100 |
+
- `reference_voice_ids`, `source_ids`, `source_corpora`: provenance
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| 101 |
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- `qc_*`: deterministic file-integrity and signal checks
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| 102 |
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| 103 |
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## Validation and limitations
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| 104 |
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| 105 |
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Every published file is checked for successful decoding, nonzero duration,
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| 106 |
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finite samples, sample rate, clipping, annotation consistency, exact class and
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| 107 |
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construction counts, split isolation, source reuse, and distribution targets.
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| 108 |
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ASR and audio-language-identification scores are intentionally not included.
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| 109 |
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Consequently, intended languages—especially in the experimental single-call
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| 110 |
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subset—are annotations of the generation request rather than guarantees from an
|
| 111 |
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independent acoustic recognizer. Human listening is recommended before using
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| 112 |
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this pilot as a gold evaluation set.
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| 113 |
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| 114 |
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## Licensing and attribution
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| 115 |
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| 116 |
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The derived dataset is released under CC BY 4.0. Users should also cite the
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| 117 |
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upstream FLEURS, KMR-Tatoeba, LibriSpeech, and OmniVoice resources when using the
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| 118 |
+
corresponding subsets. Text and source identifiers are included to support
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| 119 |
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auditing and attribution.
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| 120 |
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test/audio/segment_ara_eng_1_00415.flac
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validation_report.json
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