--- language: - en license: cc-by-4.0 task_categories: - automatic-speech-recognition - text-to-speech - audio-to-audio tags: - nano-duplex - conversational-speech - full-duplex - mimi configs: - config_name: alignment default: true data_files: - split: train path: alignment/train-*.parquet - split: validation path: alignment/validation-*.parquet - split: test path: alignment/test-*.parquet - config_name: interaction data_files: - split: train path: interaction/train-*.parquet - split: validation path: interaction/validation-*.parquet - split: test path: interaction/test-*.parquet --- # nano-duplex data v1 English speech alignment and a first natural-conversation collection for nano-duplex research. All audio is embedded in Parquet as PCM16 FLAC at 24 kHz. No external audio paths are required. | Configuration | Split | Examples | Audio hours | Speakers | | --- | --- | ---: | ---: | ---: | | alignment | train | 26,478 | 32.377 | 247 | | alignment | validation | 4,708 | 5.817 | 40 | | alignment | test | 3,624 | 4.428 | 38 | | interaction | train | 946 | 15.240 | 57 | | interaction | validation | 122 | 1.978 | 11 | | interaction | test | 116 | 1.877 | 11 | ## Sources and processing **Alignment:** [LibriTTS-R](https://www.openslr.org/141/), by Yuma Koizumi and colleagues. The complete official `train_clean_100`, `dev_clean`, and `test_clean` archives passed the official MD5 checks before extraction. Keep complete mono 24 kHz PCM16 utterances from 1 through 10 seconds with normalized transcripts. Preserve official splits and original IDs. Reject missing transcripts, out-of-range durations, duplicate audio, and entirely silent clips. WAV-to-FLAC conversion is lossless; original WAV and decoded PCM SHA256 are recorded. Original and normalized text are included; the source transcript hash refers to the original normalized-text file, before trimming its surrounding whitespace. RMS and clipping metrics are diagnostics, not human quality judgments. This is a filtered subset, not all hours implied by the upstream archive names. Citation: Koizumi et al., "LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus," 2023. **Interaction:** [otoearth/otoSpeech-full-duplex-processed-141h](https://huggingface.co/datasets/otoearth/otoSpeech-full-duplex-processed-141h). Use eight complete upstream tar shards, selected by lexicographic filename order, at revision `c635c29ad083570fe2cadbafc75770e76eded2da`. Every shard passed its pinned Hub SHA256. The selection contains 73 complete sessions. Resample each complete stereo session from 44.1 kHz to 24 kHz using SciPy polyphase filtering, then split into consecutive 60-second windows (including the shorter final window). Both channels retain the shared clock. Preserve overlapping speech and silence, and re-mute the original redacted intervals after resampling. The two `speaker_ids` are ordered by audio channel. Source-relative timestamps allow exact reassembly. Speaker-connected components stay within one split; there is no reported speaker-ID or session overlap across train/validation/test. Speaker identities beyond the IDs reported by the source have not been independently verified. Omit optional surveys and demographic profiles. Citation: otoearth, "otoSpeech-full-duplex-processed-141h," 2026. ## Scope Natural conversations have no assistant tool traces, cancelled playback plans, certified prefix-causal action targets, or transcripts in this release. `causal_verified=false` and `has_transcript=false` are explicit. These recordings support interaction preparation and analysis; they are not ready-made Stage B-D causal trajectories. No synthetic speech is substituted for real recordings. Tool, vision, and motion supervision are outside this release. `codec-pilot/` contains real Mimi ASR features and eight-codebook TTS targets for 773 training utterances (57.301 minutes), drawn from `alignment/train`. This pilot has no independent evaluation split. Its JSONL manifests reference tensor files packed in `tensors.tar`; `cache-index.json` records every member's size and SHA256. The server entry expands and verifies them automatically. The full alignment and interaction collections are processed audio; only this pilot is codec-encoded. The exact Mimi repository, revision, checkpoint digest and cache dimensions are recorded. Tensor files must be loaded with `torch.load(..., weights_only=True)` as nano-duplex does. The provided `codec-pilot/asr.toml` and `codec-pilot/tts.toml` select matching pinned assets. ## Loading ```python from datasets import load_dataset, Audio alignment = load_dataset("JonesLin/nano-duplex-data-v1", "alignment", token=True) interaction = load_dataset("JonesLin/nano-duplex-data-v1", "interaction", token=True) # Decode with soundfile without requiring torchcodec: audio = alignment["train"].cast_column("audio", Audio(decode=False))[0]["audio"] from io import BytesIO import soundfile as sf pcm, sample_rate = sf.read(BytesIO(audio["bytes"])) ``` From the nano-duplex project checkout, install `requirements-speech.txt` and `requirements-data.txt`, then use the server entry (a copy is included at `tools/hf_training.py`; it belongs in the project's `nano_duplex` package): ```bash # Download only the small pre-encoded pilot, verify and expand it, then train ASR/TTS. python -m nano_duplex.hf_training --profile pilot --steps 400 # Full Stage A: download alignment data, encode each split once, train, evaluate. python -m nano_duplex.hf_training --profile full --nproc 4 --steps 20000 ``` The first command resolves a concrete Hub commit and writes `dataset-lock.json`. Alternatively pass `--revision` with an exact commit. `--snapshot-dir`, `--work-dir`, and `--run-dir` select server storage. `--phase prepare` stops before training; `--phase train --resume` reuses data caches and resumes existing task checkpoints. An unstarted second task starts fresh. Use `--dry-run` to inspect commands without network access or file writes. H200 distributed execution has not been run here; 20,000 steps is a starting budget, not a measured convergence requirement. The full profile still performs one Mimi encoding pass over the complete alignment collection on first use. Subsequent runs reuse checked prepared manifests. Use `tools/materialize_alignment.py` separately if only mono WAV/raw JSONL export is needed. The interaction configuration is available through `load_dataset`, but is not fed into the causal duplex trainer without the additional labels described above. Changing a dataset manifest requires a new training run, not resuming an old optimizer state. ## Provenance and license Both source corpora are provided under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). This derivative retains that license and the source attributions above. The source-provided gated dataset access was obtained through the collecting user's own Hugging Face account. Source receipts, split statistics, exclusions, processing parameters, and full validation results are in `reports/`. `MANIFEST.json` inventories the exact release files with SHA256. The validation reads every packaged audio example, checks its PCM hash, duration, channel count, session continuity, redaction masks, and split isolation. No speech-quality or conversational-performance claim follows from those structural checks.