Datasets:

viai957
/

omnivoxtral-data

+---
+pretty_name: OmniVoxtral Data (Indic speech-LM corpus, working backup)
+license: other
+license_name: mixed-see-card
+license_link: https://huggingface.co/datasets/viai957/omnivoxtral-data#licence-and-rights
+language:
+- as
+- bn
+- en
+- gu
+- hi
+- kn
+- ml
+- mr
+- ne
+- or
+- pa
+- ta
+- te
+- ur
+multilinguality: multilingual
+size_categories:
+- 100K<n<1M
+task_categories:
+- automatic-speech-recognition
+- text-to-speech
+tags:
+- speech
+- speech-language-model
+- indic
+- mimi
+- neural-codec-tokens
+- interleaved-text-audio
+- whisper-pseudo-labels
+- research-only
+- omnivoxtral
+source_datasets:
+- google/fleurs
+- ai4bharat/IndicVoices
+- original
+annotations_creators:
+- machine-generated
+---
+# OmniVoxtral Data
+This repository is the working-data backup of the OmniVoxtral project, a multilingual speech language model for Indic languages and English. The base model is Mistral-7B-v0.3, extended with a Mimi-codec audio vocabulary and a depth transformer. The repository holds three kinds of material:
+- **Training-ready token shards** (`tokens_v2`): 155,861 interleaved text and audio sequences of 20 s each, with JSON sidecars.
+- **The VAD-chunked audio they were built from** (`chunks_v2`, `chunks_v2_quarantine`), along with legacy audio dumps.
+- **Pipeline artefacts and experiment outputs**: codec and tokenizer benchmarks, the SentencePiece tokenizer, the pinned validation split, the URL and search lists, the autoresearch ledger, and generated audio samples.
+> **Rights warning: read before use.** Most of the audio and pseudo-transcripts here come from YouTube recordings. The project does not hold redistribution rights to those recordings, and the project's own sidecars tag them `data_license_class: "research_only"`. A sibling card in the same project states that YouTube-derived audio "must stay private" ([`viai957/omnivoxtral-yt-indic-raw`](https://huggingface.co/datasets/viai957/omnivoxtral-yt-indic-raw), private). **This repository is currently public.** See [Licence and rights](#licence-and-rights).
+All figures were generated from the repository's own files, or from the logs of the runs that consumed them. Every number in this card is traced in `evidence.json` (next to this README in the card build directory). Anything that was never measured is marked **not measured**.
+---
+## 1. TL;DR product sheet
+| Item | Value |
+|---|---|
+| What it is | A backup of the OmniVoxtral data tree. The training-ready part is `data/tokens_v2.tar`, holding Mimi-codec and SentencePiece token sequences for speech-LM pre-training |
+| Consumer model | OmniVoxtral: a temporal transformer (Mistral-7B-v0.3, every 2nd layer pruned, LoRA r=128) plus a depth transformer over 8 Mimi codebooks |
+| Token format | 1,890 int64 tokens per clip = 90 windows × stride 21 (1 text token + 20 audio tokens per 200 ms window). Vocabulary is 81,920 = 65,536 text + 8 × 2,048 audio |
+| Size on Hub | 48 files, 191.87 GB (191,868,379,816 bytes) |
+| `tokens_v2` | 155,861 clips. 298.11 h of real speech (865.90 h including zero padding). 294,577,290 tokens |
+| Sources inside `tokens_v2` | YouTube 123,239 clips (13 Indic languages + English). IndicVoices (Hindi) 20,151. FLEURS (6 languages) 12,471 |
+| Languages in `tokens_v2` | 14: asm, ben, eng, guj, hin, kan, mal, mar, nep, ori, pan, tam, tel, urd |
+| Transcripts | 100% machine-generated (`transcript_method = whisper_large_v3`), including the IndicVoices and FLEURS clips. No human transcripts are used |
+| Raw chunked audio | `chunks_v2`: 133,517 VAD chunks, 245.52 h, from 217 source recordings. `chunks_v2_quarantine`: 39,637 chunks, 35.78 h, rejected by LID confidence below 0.7 |
+| Validation split | `val_split_v2.json`: 420 clips (30 per language × 14). **Not source-disjoint**: see §6.3 |
+| Processing compute | Not recorded in a parseable log, so **not measured** |
+| Reference training on this data | 2 runs × 20,000 steps on 4 GPUs, each about 7.5 h wall time. Final val (run 5krth3fs): total 14.589, temporal 7.604, depth 6.984 |
+| Headline data-quality results | Whisper script consistency (sampled): **asm 100% wrong-script, mal 89%**, tel 30%, pan 27%. ori: 165/165 transcripts empty. 2,779 sidecars flagged `repetition_loop` |
+| Headline downstream result | A model trained on this data (trial 22) reached free-run LID accuracy of **3/30 = 0.10** (95% Wilson CI 0.035–0.256), which is chance level for 10 languages |
+| Upload | `.hf_backup.py`, 2026-06-04/05. Last commit `f16d88f183131794ceb6b206c1e7bf7722fbff37` |
+---
+## 2. Dataset details and mechanism
+### 2.1 What the token sequences encode
+Each `.npy` in `tokens_v2` is one 20 s audio chunk. The chunk is encoded as a single stream that interleaves text and audio:
+- **Text.** Whisper-large-v3 transcribes the chunk with word timestamps. Words are bucketed at 5 Hz and tokenized with the project's 65,536-entry SentencePiece Unigram model (`data/tokenizer/omnivoxtral_sp.model`).
+- **Audio.** The 24 kHz audio is encoded by Kyutai Mimi (12.5 Hz frames, 8 residual codebooks of 2,048 entries). Codebook *q* is offset by 65,536 + 2,048·*q*, so audio and text IDs never collide.
+- **Interleave.** Each 200 ms window contains 1 text token followed by 20 audio tokens (2.5 frames × 8 codebooks), so the stride is 21.
+- **2 s text/audio offset.** The tokenizer drops the first 2 s of Mimi tokens (`mimi_num_quantizers × frame_rate × 2` = 200 tokens) and the last 2 s of text tokens (`text_hz × 2` = 10 tokens). That is a shift of 10 windows, not 2. Text for time *t* therefore sits next to the audio for *t* + 2 s, so text is delayed relative to audio. This is why 100 windows become 90, or 1,890 tokens, per 20 s chunk. The code comment reads "delay text and audio tokens by two windows … we avoid corresponding audio tokens appearing before the text tokens by accident". That comment does not match what the code does: as implemented, the audio for a moment appears in the stream 2 s *before* its text (`src/voxtral/tokenizer/model.py`, same at `c416eb4`).
+- **Padding mask.** Chunks shorter than 20 s are zero-padded. `valid_token_mask` marks the real-audio prefix, and the trainer excludes padded positions from the loss.
+```mermaid
+flowchart LR
+  subgraph Acquisition
+    S[search terms<br/>searches*.txt] --> U[YouTube URLs<br/>indic_urls*.txt]
+    U --> D[yt-dlp + ffmpeg segment<br/>chunks_indic_yt .m4a]
+    HF1[google/fleurs] --> F[FLEURS clips]
+    HF2[ai4bharat/IndicVoices] --> I[IndicVoices clips<br/>chunks_indic*]
+  end
+  D --> V[Silero VAD v6<br/>min speech 300 ms, min silence 500 ms,<br/>pad 150 ms, max 20 s]
+  V --> L{MMS-LID-2048<br/>conf >= 0.7?}
+  L -- yes --> C2[chunks_v2<br/>133,517 wav + json + lang.json]
+  L -- no --> Q[chunks_v2_quarantine<br/>39,637]
+  C2 --> M[music flags per source<br/>harmonicity + tempo]
+  C2 --> W[Whisper-large-v3 word timestamps<br/>batched faster-whisper]
+  F --> W
+  I --> W
+  W --> SP[SentencePiece Unigram 65,536<br/>5 Hz text buckets]
+  C2 --> MI[Mimi encoder<br/>12.5 Hz x 8 codebooks]
+  F --> MI
+  I --> MI
+  SP --> X[interleave: 1 text + 20 audio / 200 ms<br/>2 s text/audio offset, zero-pad to 20 s]
+  MI --> X
+  X --> T[tokens_v2 .npy + .meta.json<br/>155,861 clips]
+  T --> RL[repetition-loop flag<br/>max trigram fraction >= 0.30]
+  T --> VS[val_split_v2.json<br/>30 clips x 14 langs]
+  T --> TR[OmniVoxtral training<br/>temporal Mistral + depth transformer]
+```
+### 2.2 Repository layout (what is actually on the Hub)
+Everything is packed as tar archives because the source tree had more than 1M small files. `chunks_v2` and `autoresearch_loop` were split with `split -b 18G`. Reassemble them with `cat chunks_v2.tar.part.* | tar x`.
+| Path in repo | Bytes | Contents | Provenance / licence class |
+|---|---:|---|---|
+| `data/tokens_v2.tar` | 5,946,265,600 | 155,861 × (`.npy` + `.meta.json`), layout `tokens_v2/yt/<iso3 dir>/<2-hex shard>/<id>.*` (Nepali and Odia directories are `npi/` and `ory/`, although the sidecar `language` field says `nep`/`ori`), `tokens_v2/fleurs/<iso1>/<iso1>/<id>.*` (e.g. `fleurs/hi/hi/`), `tokens_v2/iv/hi/…/<id>.*`. See `_index/tokens_v2.jsonl` for exact paths | mixed (see §3.1) |
+| `data/chunks_v2/chunks_v2.tar.part.000`–`004` | 85,235,998,720 | 133,517 × (`.wav` 24 kHz mono + `.json` + `.lang.json`) | YouTube, research-only |
+| `data/chunks_v2_quarantine.tar` | 12,478,914,560 | 39,637 rejected chunks, same schema | YouTube, research-only |
+| `data/chunks_indic_yt.tar` | 18,703,032,320 | 57,668 raw 20 s `.m4a` chunks (pre-VAD input) | YouTube, research-only |
+| `data/chunks_indic.tar`, `chunks_indic_extra.tar` | 13,160,857,600 / 8,954,941,440 | 95,000 / 65,000 `.flac` files (count from the project's sharding README) | IndicVoices audio with no text, CC-BY-4.0 |
+| `data/chunks_indic_g{0..3}.tar`, `chunks_indic_g1_pa.tar`, `chunks_indic_ml_fix.tar`, `chunks_g{1..3}_batch2.tar` | 0.28–0.57 GB each | smaller per-language batches (24,000 files together, per the sharding README) | IndicVoices audio with no text, CC-BY-4.0 |
+| `data/codec_benchmark_results.tar` | 194,560 | Mimi round-trip benchmark (`raw_results.csv`, `summary.md`, `ANALYSIS.md`) | project-created |
+| `data/tokenizer.tar` | 12,072,960 | `omnivoxtral_sp.model`/`.vocab`, `training_corpus.txt` (FLEURS train transcripts), `comparison.md` | project-created; corpus text from FLEURS (CC-BY-4.0) |
+| `data/tokenizer_benchmark_results.tar` | 358,400 | Mistral BPE fertility per language | project-created |
+| `data/dataset_inventory.tar` | 1,157,120 | 3,500-sample, 8.8 h survey of FLEURS and IndicVoices across 22 languages (2026-03-13) | project-created |
+| `data/indic_test_samples.tar` | 141,588,480 | test WAVs used by the codec benchmark | FLEURS / IndicVoices / synthetic placeholders |
+| `data/yt_g{0..3}.tar` | 20,480 each | empty shard-directory skeletons (no files) | n/a |
+| `data/.cache.tar` | 10,240 | empty cache stub | n/a |
+| `data/_loose_files.tar` + top-level copies | about 0.43 MB | `searches.txt` (538 lines), `searches_indic.txt` (139), `indic_searches.txt` (193), `indic_urls.txt` (1,845 URLs), `indic_urls_new.txt` (1,501), `indic_urls_all.txt` (3,219), `source_music_flags.json` (+4 per-rank parts), `val_split_v2.json` | project-created; URLs point to third-party content |
+| `experiments/autoresearch_loop/*.tar.part.000`–`002` | 43,716,782,080 | autoresearch loop code, per-trial run directories, SQLite ledger, rejected checkpoints | project-created |
+| `experiments/outputs.tar` | 32,317,440 | 4 sets of 14 generated WAV samples (one per language) from the two reference runs (§5) | model outputs |
+| `autoresearch_snapshot.tar.gz` | 13,611,385 | snapshot of `autoresearch/` (memory files, `results.tsv` with 528 experiment rows) | project-created |
+There is no `datasets` loading script, and the Hub viewer cannot read these archives. To use the data, download the tars and read the `.npy` and `.json` files directly (§9).
+---
+## 3. Sources, schema, splits, statistics
+### 3.1 Sources and licences
+| Source | Where | Upstream licence | Sidecar tag | Notes |
+|---|---|---|---|---|
+| YouTube recordings (podcasts, interviews, talks) found with project search terms | `chunks_indic_yt`, `chunks_v2`, `chunks_v2_quarantine`, `tokens_v2/yt` | copyright of the original rights holders; no redistribution licence held | `research_only` | 217 source recordings back `chunks_v2`. `source_url` is `null` in the sidecars, so per-clip provenance cannot be recovered from this repo alone |
+| FLEURS (`google/fleurs`) | `tokens_v2/fleurs` | CC-BY-4.0 | `apache` (a code default in the sidecar writer, **not** the upstream licence) | 12,471 clips in 6 languages |
+| IndicVoices (`ai4bharat/IndicVoices`) | `tokens_v2/iv` (Hindi), `chunks_indic*`, `chunks_g*_batch2` | CC-BY-4.0 | `cc-by-4.0` | 20,151 Hindi clips in tokens. The upstream human transcripts were **not** used; text was re-generated with Whisper |
+### 3.2 Schema
+**`tokens_v2/<group>/<lang>/<shard>/<id>.npy`** holds `int64[1890]`. Position *k* with *k* mod 21 = 0 is a text token (ID < 65,536). The other 20 positions are audio tokens (codebook *q* in [65,536 + 2,048*q*, 65,536 + 2,048(*q*+1))).
+**`<id>.meta.json`** (schema_version 2). An abridged real example from `tokens_v2/yt/hin/00/00045e3004d24a8ab24fc07126704adc.meta.json`:
+```json
+{
+  "schema_version": 2, "preprocessing_version": "v2.0",
+  "source": "youtube", "source_id": "00045e3004d24a8ab24fc07126704adc", "source_url": null,
+  "language": "hin", "language_confidence": 0.9846662282943726, "language_method": "mms_lid_2048",
+  "transcript": " पहला पास साल ऐसा रहा लोगों ने धूमधाम से पूजा किया, लोगों ने धूमधाम से किर्तन किया.",
+  "transcript_method": "whisper_large_v3", "transcript_avg_logprob": -0.05110206234073916,
+  "duration_s": 20.0, "sample_rate": 24000, "num_channels": 1, "stream_layout": "single",
+  "snr_db": null, "speech_ratio": null, "clip_ratio": null, "music_likely": null,
+  "tokenizer_config": {"sp_model": "omnivoxtral_sp.model", "text_hz": 5, "mimi_num_quantizers": 8, "stride": 21},
+  "token_count": 1890, "valid_token_mask": [true, "... 1890 booleans ..."],
+  "data_license_class": "research_only", "quarantine_reason": null, "task": "transcribe"
+}
+```
+`duration_s` is always the padded length (20.0). To get the real speech length, use `duration_s × mean(valid_token_mask)`.
+**`chunks_v2/yt/unknown/<shard>/<id>.json`** holds `source`, `source_id` (`<recording-uuid>_<segment>`), `chunk_index`, `start_s_in_source`, `end_s_in_source`, `duration_s`, `num_channels`, `stream_role`, and `language` (always `null` here). **`<id>.lang.json`** holds `language`, `confidence`, `method` (`mms_lid_2048`), `secondary`, `agreement`, and `chunk_duration_s`.
+**`val_split_v2.json`** holds `seed` (1729), `per_lang_target` (30), `min_lid_confidence` (0.7), `per_language`, `per_language_per_source`, `missing_tier1` (san, sin, snd, bod, doi, kok, mni, sat, kas), and `files` (420 paths).
+### 3.3 Splits
+| Split | Clips | Definition |
+|---|---:|---|
+| train | 155,441 | every `tokens_v2` file not in `val_split_v2.json` |
+| val | 420 | 30 per language × 14 languages, sampled at clip level with seed 1729 and LID confidence ≥ 0.7. Mix per language: hin = 10 FLEURS + 10 IV + 10 YT; ben/kan/mal/tam/tel = 15 FLEURS + 15 YT; all others = 30 YT |
+| test | none | no held-out test split exists |
+### 3.4 Statistics (`tokens_v2`)
+![tokens_v2 language distribution](figures/tokens_v2_language_distribution.png)
+*Figure 1. Left: hours of real speech per language (sum of `duration_s × mean(valid_token_mask)`). Right: clip counts. Source: every `*.meta.json` inside `data/tokens_v2.tar`, indexed by streaming the archive from this repo (`_tools/stream_index.py`, `_tools/stats_tokens.py` → `_stats/tokens_v2_stats.json`).*
+| Group | Lang | Clips | Speech h | Padded h | Median LID conf | Empty transcripts |
+|---|---|---:|---:|---:|---:|---:|
+| yt | tam | 27,732 | 56.74 | 154.07 | 0.9997 | 5 |
+| yt | hin | 25,696 | 43.93 | 142.76 | 0.9785 | 0 |
+| yt | ben | 20,322 | 28.17 | 112.90 | 0.9999 | 0 |
+| yt | asm | 9,642 | 20.06 | 53.57 | 0.9998 | 4 |
+| yt | kan | 6,828 | 14.38 | 37.93 | 0.9991 | 1 |
+| yt | urd | 6,053 | 12.94 | 33.63 | 0.9473 | 0 |
+| yt | tel | 6,694 | 12.30 | 37.19 | 0.9966 | 0 |
+| yt | mar | 5,857 | 11.12 | 32.54 | 0.9995 | 0 |
+| yt | pan | 4,846 | 8.18 | 26.92 | 0.9932 | 0 |
+| yt | guj | 3,453 | 8.02 | 19.18 | 0.9997 | 0 |
+| yt | mal | 2,731 | 5.02 | 15.17 | 0.9998 | 0 |
+| yt | eng | 2,838 | 4.66 | 15.77 | 0.9969 | 0 |
+| yt | nep | 382 | 0.71 | 2.12 | 0.9997 | 0 |
+| yt | ori | 165 | 0.46 | 0.92 | 0.9881 | **165** |
+| iv | hin | 20,151 | 29.64 | 111.95 | (dataset label) | 0 |
+| fleurs | mal | 2,931 | 9.40 | 16.28 | (dataset label) | 0 |
+| fleurs | tam | 2,367 | 8.41 | 13.15 | (dataset label) | 0 |
+| fleurs | kan | 2,283 | 8.07 | 12.68 | (dataset label) | 0 |
+| fleurs | tel | 2,302 | 7.75 | 12.79 | (dataset label) | 0 |
+| fleurs | hin | 2,120 | 6.51 | 11.78 | (dataset label) | 0 |
+| fleurs | ben | 468 | 1.64 | 2.60 | (dataset label) | 0 |
+| **total** | 14 langs | **155,861** | **298.11** | **865.90** | | |
+The real speech per 20 s window has a mean of 6.62 s for YT (median 4.89), 5.30 s for IV (median 3.78) and 12.07 s for FLEURS (median 11.56). Across the corpus, **about 66% of token positions are padding**.
+![tokens_v2 quality distributions](figures/tokens_v2_quality_distributions.png)
+*Figure 2. Left: real speech seconds per window by source (log y). Middle: MMS-LID-2048 confidence for the 123,239 YouTube clips (minimum 0.70002; the 0.7 quarantine gate is visible). Right: Whisper `transcript_avg_logprob` for the 120,786 YT clips with a non-zero value (median −0.1506; the remaining 2,453 store 0.0 or nothing, because of a faster-whisper adapter bug documented in the project notes). Source: `_index/tokens_v2.jsonl` → `_stats/tokens_v2_distributions.json`.*
+### 3.5 Statistics (`chunks_v2` and quarantine)
+![chunks_v2 duration and LID](figures/chunks_v2_duration_lid.png)
+*Figure 3. Left: VAD chunk durations from the `.json` sidecars (accepted vs quarantined, log y). Right: top-25 MMS-LID top-1 labels over accepted chunks (log y). Source: `chunks_v2` sidecars and `chunks_v2_quarantine.tar` (streamed from this repo) → `_stats/chunks_stats.json`.*
+| | `chunks_v2` (accepted) | `chunks_v2_quarantine` |
+|---|---:|---:|
+| Chunks | 133,517 | 39,637 |
+| Audio hours | 245.52 | 35.78 |
+| Mean / median duration (s) | 6.62 / 4.90 | 3.25 / 1.89 |
+| Min / max duration (s) | 0.2 / 20.0 | 0.1 / 20.0 |
+| Median LID confidence | 0.9984 | 0.4026 |
+| Chunks with LID confidence < 0.7 | 0 | 39,637 (all) |
+| Distinct LID top-1 labels | 197 | 702 |
+| Source recordings | 217 | 217 |
+The accepted chunks are spread across 197 distinct LID labels. Only 14 languages were tokenized. Accepted chunks in other labels were not tokenized, for example san 2,971, npi 2,900 (of which 382 tokenized as `nep`), ory 1,259 (165 tokenized as `ori`), sin 531, snd 462 and ara 317. Of the 133,517 accepted chunks, 123,239 appear in `tokens_v2/yt`.
+The sidecars come from the local `chunks_v2` tree, which produced the uploaded tar. A partial re-index of the uploaded tar found byte-identical sidecars: 34,843 of 34,843 compared (26% of the archive) were identical and none were missing locally. The quarantine numbers come from the uploaded tar itself.
+### 3.6 Language mixture used in training
+![sampling weights](figures/language_sampling_weights.png)
+*Figure 4. Natural share of training clips vs the temperature-sampled share π_l ∝ n_l^{1/τ} with τ = 3.3 (the `VoxtralDataset` default at the reference-run commit `c416eb4`). Source: `_stats/sampling_weights.json`, computed from the clip counts in Figure 1 minus `val_split_v2.json`.*
+Temperature sampling compresses Hindi from 30.8% to 12.8% of draws and raises Odia from 0.09% to 2.2%. Odia's 135 training clips all have empty transcripts.
+### 3.7 Filtering and decontamination
+- **VAD.** Silero VAD v6 with min speech 300 ms, min silence 500 ms, speech pad 150 ms and max segment 20 s. Mono mixdown applies unless the stereo L/R correlation is below 0.5 (`scripts/vad_chunker.py`).
+- **LID gate.** MMS-LID-2048 runs on every chunk. A chunk is quarantined if its confidence is below 0.7 or the primary and secondary predictions disagree (`scripts/detect_language.py`).
+- **Music.** Per-source harmonicity plus tempo heuristic over 217 sources, of which 49 are flagged `music_likely` (`source_music_flags.json`). The flag was intended to gate Demucs. It is **not** applied as a training filter, and `music_likely` is `null` in every sampled token sidecar.
+- **Repetition loops.** 2,779 token sidecars carry `quarantine_reason = "repetition_loop"` (max trigram fraction ≥ 0.30). The loader at the reference-run commit `c416eb4` has no quarantine filter. A later commit (`9015984`) adds one, so whether the §5 runs excluded these clips cannot be verified.
+- **SNR / speech-ratio / clipping.** The `snr_db`, `speech_ratio` and `clip_ratio` fields exist but are `null` in all 155,861 sidecars (full scan of `_index/tokens_v2.jsonl`), so they were **not measured**.
+- **Decontamination against public benchmarks.** **Not performed.** FLEURS *train* and *test* clips may both appear in `tokens_v2/fleurs`, because the split of origin is not stored in the sidecars. The tokenizer was trained on FLEURS train and benchmarked on FLEURS test.
+---
+## 4. Processing procedure and the objective this data feeds
+### 4.1 Processing stages (as run)
+| Stage | Tool / setting | Output |
+|---|---|---|
+| Index | project search terms → YouTube search (min duration 30 min) | `indic_urls*.txt` |
+| Scrape | `yt-dlp` + `ffmpeg segment` (stream copy, no transcode) | `chunks_indic_yt` (57,668 × 20 s `.m4a`) |
+| VAD chunk | Silero VAD v6 (settings in §3.7), resample to 24 kHz | `chunks_v2` |
+| LID | MMS-LID-2048, gate at 0.7 | `.lang.json`, `chunks_v2_quarantine` |
+| ASR + alignment | Whisper-large-v3 word timestamps. YouTube used batched faster-whisper (8 same-language clips concatenated per call); Odia is unsupported by faster-whisper, so its transcripts are empty | `transcript` |
+| Text tokenizer | SentencePiece Unigram, vocab 65,536, `character_coverage` 0.9999, trained on FLEURS-train transcripts (`training_corpus.txt`, 8.56 MB); 23 language tags (IDs 4–26) + 6 control tokens | `omnivoxtral_sp.model` |
+| Audio tokenizer | Kyutai Mimi, 8 of its RVQ codebooks, 12.5 Hz | audio tokens |
+| Interleave | 5 Hz text, stride 21, 2 s text/audio offset (first 2 s audio + last 2 s text dropped), zero-pad to 20 s, `valid_token_mask` | `tokens_v2` |
+| Post-hoc QC | repetition-loop flag (trigram ≥ 0.30), pinned val split | sidecar fields, `val_split_v2.json` |
+Index timestamps: the newest `tokens_v2` member is dated 2026-05-06 09:49 UTC, and the newest `chunks_v2` member 2026-05-05 18:01 UTC. **Processing compute and wall time were not measured.** The project notes give throughput for batched faster-whisper (113 files/min/rank in a smoke test), but there is no end-to-end log.
+### 4.2 Training objective that consumes these tokens
+The objective below is transcribed from `src/voxtral/trainer/omni_trainer.py` at commit `c416eb4`, which is the commit the §5 runs logged.
+Let $x_{1..N}$ be the 1,890-token sequence and $m_t \in \{0,1\}$ the `valid_token_mask`. The temporal transformer predicts $x_{t+1}$. Position weights are indexed by target position $k$ (`full_weights[1:]`) and repeat a stride-21 pattern
+$$
+\omega_k=\begin{cases}w_{\text{text}} & k \bmod 21 = 0\\ w_{\text{sem}} & ((k \bmod 21)-1) \bmod 8 = 0\\ w_{\text{ac}} & \text{otherwise}\end{cases},\qquad \tilde\omega_t=\frac{\omega_{t}}{\frac1N\sum_s \omega_s}
+$$
+$$
+\mathcal L_{\text{temporal}}=\frac{\sum_t m_t\,\tilde\omega_t\,\bigl(-\log p_\theta(x_{t+1}\mid x_{\le t})\bigr)}{\sum_t m_t}
+$$
+At each audio frame $f$, the depth transformer predicts the 8 codebooks autoregressively, using focal cross-entropy with $\gamma = 2$, a per-codebook weight $\alpha_0 = 100$, $\alpha_{1..7} = 1$, and RVQ dropout applied only during training, with $d_0 = 1$ (q0 is never dropped, `drop_mask[0] = True`) and $d_{1..7} \sim \text{Bernoulli}(0.7)$:
+$$
+\ell_q=\frac{\sum_f m_f\,(1-p_{f,q})^{\gamma}\,(-\log p_{f,q})}{\sum_f m_f},\qquad
+\mathcal L_{\text{depth}}=\frac{\sum_q \alpha_q d_q\,\ell_q}{\sum_q \alpha_q d_q},\qquad
+\mathcal L=\mathcal L_{\text{temporal}}+\mathcal L_{\text{depth}}
+$$
+Language mixing is $\pi_l = n_l^{1/\tau} / \sum_{l'} n_{l'}^{1/\tau}$ with $\tau = 3.3$.
+No preference optimisation, RL, reward model or PRM was applied to this data. Those methods do not apply to this repository.
+### 4.3 Reference-run hyperparameters (from W&B `config.yaml`)
+| Hyperparameter | 5krth3fs | dq7kimwg |
+|---|---|---|
+| Base | `mistralai/Mistral-7B-v0.3`, `prune_layers=2` (temporal 4,550,955,008 params) | same |
+| Adapter | LoRA r=128 (no DoRA) | same |
+| Depth transformer | 4 layers, 16 heads, dim 1024 (88,351,744 params) | same |
+| `loss_weights` [text, sem, ac] | [100, 1, 1] | [10, 1, 1] |
+| Depth | focal γ=2, mask rate 0.15, q-dropout 0.3, label smoothing 0 | same |
+| Optimiser | AdamW β=(0.9, 0.95), ε=1e-9, weight decay 0 | same |
+| LR | peak 3e-4, warm-up 500, decay to 0 at 20,000 (as logged) | peak 1e-4, same schedule |
+| Batch | 1 per rank × 4 ranks (DDP), grad-accum 1 | same |
+| Grad clip | 1.0 | same |
+| Steps | 20,000 | 20,000 |
+| Data | `./data/tokens_v2` + pinned `val_split_v2.json`; val every 500 steps on 50 batches | same |
+| Precision / memory | bf16, gradient checkpointing, no `torch.compile` | same |
+| Seed | 42 | 42 |
+| Hardware | 4 GPUs. W&B records "NVIDIA A10G"; a later audit of this host (2026-10-25) found 4× NVIDIA L4 | same |
+| Wall time | 26,936 s (7.48 h) | 26,922 s (7.48 h) |
+| Code | `c416eb4bd6ed7e465d3c6f8bfe7160a592326f62` (W&B git record) | same |
+---
+## 5. Training curves (reference runs on this data)
+![reference training curves](figures/reference_training_curves.png)
+*Figure 5. The two 20k-step OmniVoxtral runs on `data/tokens_v2`. Thin lines: train losses averaged over 50 steps. Thick lines: val every 500 steps. The bottom panels show the logged LR, depth-transformer token accuracy (q0 and q1–7), and text-token accuracy. Sources: `logs/wandb/run-20260506_201931-5krth3fs/files/output.log` and `logs/wandb/run-20260507_040612-dq7kimwg/files/output.log`, parsed by `_tools/plot_curves.py` → `_stats/reference_runs.json`. The total and temporal losses are weighted by `loss_weights` and are **not comparable across the two runs**. The depth loss uses identical settings in both runs and is comparable.*
+| Metric | 5krth3fs | dq7kimwg |
+|---|---:|---:|
+| val total @500 → @20k | 14.813 → 14.589 | 14.996 → 10.609 |
+| best val total (step) | 14.209 (5,000) | 10.596 (15,500) |
+| val temporal @20k (best, step) | 7.604 (7.360, 16,000) | 5.682 (5.674, 14,500) |
+| **val depth @20k** (comparable) | **6.984** | **4.927** |
+| train depth-acc q1–7 @20k (max) | 0.307 (0.346) | 0.328 (0.367) |
+| train depth-acc q0 @20k | 0.182 | 0.242 |
+| train text-token accuracy (max over run) | 0.000 (0.001) | 0.000 (0.001) |
+| 50-step windows with `grad_norm=inf` | 400/400 | 400/400 |
+There is also a model-side hill-climb over the project's experiment ledger, which ships in `autoresearch_snapshot.tar.gz`:
+![autoresearch ledger](figures/autoresearch_ledger_hillclimb.png)
+*Figure 6. `total_loss` for the 522 numeric rows of `autoresearch/memory/results.tsv` (495 keep, 21 discard, 3 crash, 3 oom_risk), with the running best of kept rows. The rows mix fake-data smoke tests, 1-layer to 7B models, the earlier v1 token set and changing loss definitions, so this is a chronology of logged numbers, **not** a controlled hill-climb on this dataset. Source: `autoresearch_snapshot.tar.gz` → `_stats/autoresearch_ledger.json`.*
+---
+## 6. Evaluation
+### 6.1 Codec fidelity on Indic speech (Mimi round trip)
+![codec benchmark](figures/codec_benchmark_mimi.png)
+*Figure 7. Mimi encode/decode with 4 and 8 codebooks on 10 clips per language for the 13 real languages. PESQ is wideband at 16 kHz. Source: `data/codec_benchmark_results/raw_results.csv` (2026-03-13). Nine further languages in the same file are synthetic sine placeholders and are excluded.*
+| Metric (mean over 13 langs, n=10 each) | 4 codebooks | 8 codebooks (used) |
+|---|---:|---:|
+| PESQ | 1.44 | 1.85 (range 1.41 ori – 2.16 guj) |
+| F0 correlation | 0.892 | 0.943 (min 0.831 nep) |
+| Energy correlation | 0.963 | 0.983 |
+The benchmark's own pass threshold (PESQ ≥ 3.0) was met by 0/22 languages. The project kept Mimi at 8 codebooks after recalibrating against Moshi's reported English PESQ of 2.45, as cited in `ANALYSIS.md`. No subjective listening test (MOS) was run, so it is **not measured**. Confidence intervals were not computed (n=10 per cell).
+### 6.2 Text-tokenizer fertility
+![tokenizer fertility](figures/tokenizer_fertility.png)
+*Figure 8. Mean tokens per whitespace word on up to 50 FLEURS-test sentences per language. The SentencePiece model was trained only on FLEURS-train transcripts. Source: `data/tokenizer/comparison.md`.*
+Mean fertility drops from **10.79** (Mistral BPE, 32,768 vocab) to **1.91** (OmniVoxtral SP, 65,536). The largest gains are for Malayalam (22.20 → 2.49) and Odia (18.19 → 2.05).
+### 6.3 Transcript and split quality
+![script audit](figures/transcript_script_audit.png)
+*Figure 9. Fraction of sampled YouTube transcripts whose characters are mostly outside the expected script (phase-0 audit, 2026-05-07; *n* per bar). Source: `autoresearch_loop/runs/phase0_audit.json` (inside `experiments/autoresearch_loop`).*
+| Check | Result | Source |
+|---|---|---|
+| Wrong-script transcripts (sampled) | asm 33/33 (1.00), mal 59/66 (0.89), tel 20/66 (0.30), pan 9/33 (0.27), guj 4/33 (0.12), ben 4/66 (0.06), hin 3/99 (0.03); eng, kan, mar, tam, urd 0 | `phase0_audit.json` |
+| Empty transcripts | ori 165/165; tam 5, asm 4, kan 1 | `tokens_v2` sidecars |
+| Repetition-loop transcripts | 2,779 flagged (1.8% of clips) | `tokens_v2` sidecars |
+| `transcript_avg_logprob` 0.0 or absent (adapter bug) | 2,453 of 123,239 YT clips; all IV and FLEURS clips | `tokens_v2` sidecars |
+| Val split source-disjointness | **Fails.** The 325 YT val clips come from 134 source recordings. All 134 also contribute training clips, and 94,875 of 122,914 YT training clips share a recording with some val clip | `val_split_v2.json` × `chunks_v2` `source_id` |
+| Human-transcript WER of the pseudo-labels | **not measured** | none |
+| Speaker / gender / dialect balance | **not measured** (no speaker labels) | none |
+### 6.4 Downstream: a model trained on this data
+| Model (all trained on `tokens_v2`) | Eval | n | Result |
+|---|---|---:|---|
+| 5krth3fs, step 20k | val losses (in-recording, see 6.3) | 50 batches | total 14.589, temporal 7.604, depth 6.984 |
+| dq7kimwg, step 20k | same | 50 batches | total 10.609, temporal 5.682, depth 4.927 |
+| trial 22 (PURE-NLL: loss weights [1,1,1], γ=0, LR 1e-4), step 2,000 | free-run generation, Whisper LID on generated audio, 10 languages × 3 seeds, T=0.8, top-k 250 | 30 | LID accuracy **0.10** (3/30; 95% Wilson CI 0.035–0.256; the only correct language was English, 3/3). Silent rate 0.40. Non-empty ASR transcript rate 0.90 |
+| 5krth3fs / dq7kimwg generated samples | none | none | audio is in `experiments/outputs.tar`; **not scored** |
+No standard speech benchmark (FLEURS ASR WER, TTS MOS, Full-Duplex-Bench, and so on) was run on models trained from this data, so none is reported.
+---
+## 7. Discussion: negative results and lessons
+1. **The pseudo-labels are the weakest link.** All text in `tokens_v2` is Whisper-large-v3 output, including the IndicVoices clips, whose human transcripts were available upstream and were not used. For Assamese, the sampled transcripts were 100% in the wrong script (33/33 sampled; examples include English, Gurmukhi and Devanagari text), Malayalam was 89%, and Odia transcripts are empty. The text stream for these languages teaches the wrong target. Mask or re-transcribe them before you rely on text loss.
+2. **Two-thirds of the sequence is padding.** Fixed 20 s windows around VAD chunks with median length 4.9 s leave about 66% of positions masked. Packing several chunks per window would roughly triple the useful tokens per step.
+3. **The val split measures memorisation of recordings, not generalisation.** All 217 recordings appear in train, and every YT val clip shares its recording with training clips. Lower val loss on this split should not be read as transfer to new speakers or channels. Rebuild the split by `source_id` prefix.
+4. **Concentration.** Only 217 source recordings back 123,239 YT clips. Tamil and Hindi together are 50% of clips (natural share), and nep and ori have 382 and 165 clips. Temperature sampling (τ = 3.3) up-weights the tails, but it cannot add speakers.
+5. **Loss went down while the model learned no language identity.** Across more than 500 logged trials on this corpus family, teacher-forced loss fell while free-run LID stayed at chance (§6.4). Project diagnostics traced this to a model-side defect, not a data defect: the SentencePiece language-tag rows (IDs 10–26) inherited all-zero Mistral embedding rows and were never initialised. The lesson for users of this data is that val loss alone is not evidence of a usable model. Gate on a generation-side metric.
+6. **`grad_norm=inf` in every logging window of both runs.** This happens under both `loss_weights=[100,1,1]` (`5krth3fs`) and `[10,1,1]` (`dq7kimwg`, 400/400 windows), so the weights are not the cause. A later root-cause analysis (2026-05-23, `_safe_grad_norm` docstring in `omni_trainer.py` at `9015984`) found that the depth-transformer backward injects a gradient of about 5e33 into the temporal embedding. The fp32 sum of squares then overflows to inf. The `isfinite` skip guard active at run time would then skip `optimizer.step()`. **The curves shown may therefore reflect far fewer optimiser updates than the step count suggests.** The eventual fix is overflow-proof per-parameter fp64 clipping (`9015984`). Text-token accuracy never left 0.000–0.001 in either run.
+7. **The codec is a ceiling.** Mimi at 8 codebooks reaches mean PESQ 1.85 on Indic read speech. Any model trained on these tokens inherits that reconstruction ceiling.
+---
+## 8. Intended use, limitations, risks
+**Intended use.** This is an internal research backup for reproducing OmniVoxtral speech-LM experiments: inspecting the token format, reproducing the reference runs, and auditing the pipeline.
+**Out of scope.** Do not use it for:
+- redistributing the YouTube-derived audio or transcripts;
+- commercial training;
+- evaluating ASR quality, since the labels are machine-generated;
+- speaker identification or any use that profiles individuals.
+**Limitations.**
+- Machine transcripts only.
+- Weak language labels: LID comes from audio, and the `chunks_v2` sidecars carry `language: null`.
+- No speaker, gender or dialect metadata.
+- `source_url` is null, so YouTube provenance is not recoverable per clip.
+- No SNR or music filtering applied.
+- The val split is not source-disjoint.
+- Nine Tier-1 languages are missing from tokens: san, sin, snd, bod, doi, kok, mni, sat, kas.
+**Risks.**
+- **Copyright.** The YouTube audio belongs to third parties. Hosting it publicly redistributes it.
+- **Privacy.** The recordings contain identifiable voices of people who did not consent to inclusion.
+- **Bias.** The corpus is skewed toward the content and speakers of 217 recordings.
+- **Mislabelled licences.** FLEURS sidecars carry `apache`, while the upstream licence is CC-BY-4.0.
+---
+## 9. Reproducibility
+```bash
+# download (use a disk with >200 GB free; never / or /tmp)
+huggingface-cli download viai957/omnivoxtral-data --repo-type dataset --local-dir ./omnivoxtral-data
+# reassemble split archives
+cat data/chunks_v2/chunks_v2.tar.part.* | tar -x
+cat experiments/autoresearch_loop/autoresearch_loop.tar.part.* | tar -x
+tar -xf data/tokens_v2.tar
+# read one clip
+python - <<'EOF'
+import json, numpy as np
+p = "tokens_v2/yt/hin/00/00045e3004d24a8ab24fc07126704adc"
+x = np.load(p + ".npy"); m = json.load(open(p + ".meta.json"))
+text = x[0::21]; audio = x.reshape(90, 21)[:, 1:]        # 90 windows x (1 text + 20 audio)
+speech_s = m["duration_s"] * np.mean(m["valid_token_mask"])
+print(x.shape, m["language"], round(speech_s, 2), m["transcript"][:60])
+EOF
+# reference run 5krth3fs, reconstructed from its logged config.yaml (OmniVoxtral repo near c416eb4; env-var config via pydantic-settings)
+DATA_PATH=./data/tokens_v2 MISTRAL_PRETRAINED_PATH=mistralai/Mistral-7B-v0.3 PRUNE_LAYERS=2 \
+LORA_RANK=128 LR=3e-4 LOSS_WEIGHTS='[100,1,1]' MAX_STEPS=20000 WARMUP_STEPS=500 WEIGHT_DECAY=0 \
+DEPTH_NUM_LAYERS=4 DEPTH_FOCAL_GAMMA=2.0 DEPTH_MASK_RATE=0.15 DEPTH_Q_DROPOUT=0.3 GRADIENT_CHECKPOINTING=true SEED=42 \
+torchrun --nproc_per_node=4 scripts/train_omni.py
+```
+| Artefact | Identifier |
+|---|---|
+| Hub revision documented here | `f16d88f183131794ceb6b206c1e7bf7722fbff37` (2026-06-05) |
+| Upload script | `.hf_backup.py` (OmniVoxtral repo root) and `.hf_backup.log` |
+| Code that trained the reference runs | `c416eb4bd6ed7e465d3c6f8bfe7160a592326f62`, as recorded by W&B. The working tree was not clean: `scripts/retokenize_v2_batched.py`, which produced the YT tokens, is first committed in `9015984`. The non-finite-gradient step skip was first committed in `56ae3c6` (2026-05-13), but it was an uncommitted working-tree edit when the May 6–7 runs were launched. Treat `c416eb4` as approximate |
+| Code snapshot at upload time | `90159847a4625420465fb99ee82a6719e95f3b82` (2026-06-05, adds the quarantine-aware loader and `quarantine_repetitions.py`) |
+| Pipeline scripts | `scripts/vad_chunker.py`, `scripts/detect_language.py`, `scripts/detect_music.py`, `scripts/retokenize_v2_batched.py`, `scripts/quarantine_repetitions.py`, `scripts/build_val_split.py`, `scripts/train_tokenizer.py`, `scripts/benchmark_codec.py` |
+| Card statistics | `_tools/stream_index.py`, `_tools/stats_tokens.py`, `_tools/stats_chunks.py`, `_tools/plot_*.py`; outputs in `_stats/*.json` |
+The token shards can be regenerated only partially. The YouTube source audio has no stored URLs, and the IndicVoices and FLEURS text would differ if re-transcribed with a different Whisper build.
+---
+## 10. Related repositories, licence, citation
+**Sibling repositories (viai957).**
+- [`viai957/omnivoxtral-checkpoints`](https://huggingface.co/viai957/omnivoxtral-checkpoints) (private): checkpoints from runs on this data.
+- [`viai957/omnivoxtral-yt-indic-raw`](https://huggingface.co/datasets/viai957/omnivoxtral-yt-indic-raw) (private): WebDataset re-shard of the YouTube audio (`chunks_v2`, `chunks_indic_yt`) with a parquet manifest.
+- [`viai957/omnivoxtral-indic-artifacts`](https://huggingface.co/datasets/viai957/omnivoxtral-indic-artifacts) (private): project-created artefacts only (codec and tokenizer benchmarks, tokenizer, synthetic duplex).
+- [`viai957/indic-duplex-speech-artifacts`](https://huggingface.co/datasets/viai957/indic-duplex-speech-artifacts) (public).
+### Licence and rights
+This repository has no single licence, so the YAML uses `license: other`.
+- **YouTube-derived material** (`chunks_indic_yt`, `chunks_v2`, `chunks_v2_quarantine`, `tokens_v2/yt`, `indic_urls*.txt`). Copyright remains with the original rights holders, and the project holds no licence to redistribute it. It is research-only. The project's own guidance for this material is that it stays private.
+- **IndicVoices-derived material** (`tokens_v2/iv`, `chunks_indic*`, `chunks_g*_batch2`). CC-BY-4.0, © AI4Bharat. Attribution required.
+- **FLEURS-derived material** (`tokens_v2/fleurs`, `data/tokenizer/training_corpus.txt`). CC-BY-4.0, © Google. Attribution required.
+- **Project-created material** (benchmarks, tokenizer model, scripts, ledgers, split files). Released by the project. The upstream OmniVoxtral/Voxtral code is MIT.
+- **Mimi codec** (Kyutai), used to produce the audio tokens: see the Kyutai model licence.
+### Citation
+```bibtex
+@misc{omnivoxtral_data_2026,
+  title        = {OmniVoxtral Data: interleaved Mimi/SentencePiece token corpus for Indic speech-language modelling (working backup)},
+  author       = {{viai957}},
+  year         = {2026},
+  howpublished = {\url{https://huggingface.co/datasets/viai957/omnivoxtral-data}},
+  note         = {Revision f16d88f183131794ceb6b206c1e7bf7722fbff37}
+}
+@article{conneau2022fleurs,
+  title   = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech},
+  author  = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur},
+  journal = {arXiv preprint arXiv:2205.12446}, year = {2022}
+}
+@article{javed2024indicvoices,
+  title   = {IndicVoices: Towards building an Inclusive Multilingual Speech Dataset for Indian Languages},
+  author  = {Javed, Tahir and others},
+  journal = {arXiv preprint arXiv:2403.01926}, year = {2024}
+}
+@article{defossez2024moshi,
+  title   = {Moshi: a speech-text foundation model for real-time dialogue},
+  author  = {D{\'e}fossez, Alexandre and others},
+  journal = {arXiv preprint arXiv:2410.00037}, year = {2024}
+}
+@article{radford2022whisper,
+  title   = {Robust Speech Recognition via Large-Scale Weak Supervision},
+  author  = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
+  journal = {arXiv preprint arXiv:2212.04356}, year = {2022}
+}
+@article{pratap2023mms,
+  title   = {Scaling Speech Technology to 1,000+ Languages},
+  author  = {Pratap, Vineel and others},
+  journal = {arXiv preprint arXiv:2305.13516}, year = {2023}
+}
+```