--- license: cc-by-nc-4.0 language: - en task_categories: - text-to-speech - automatic-speech-recognition tags: - expressive-speech - expresso - emotional-speech - style-transfer - multi-speaker pretty_name: Expresso (audio + text) size_categories: - 10K ⚠️ **License: CC-BY-NC-4.0** — non-commercial use only. ## Configs - **`read`** — 11.6k mono read-speech utterances with **human transcripts**. - **`conversational`** — ~15.9k mono per-utterance turns derived from the stereo conversational dialogues, transcribed with **Whisper Large V3 Turbo**. ## `read` config 11.6k mono utterances at **48 kHz / 24-bit**, fully transcribed by humans. | | train | dev | test | |---|---|---|---| | rows | 10,388 | 628 | 588 | ### Schema | Column | Type | Notes | |---|---|---| | `id` | string | e.g. `ex01_confused_00001`; longform chunks: `ex01_default_longform_00001__0-16.49` | | `audio` | Audio @ 48 kHz mono | | | `text` | string | human-written transcription (mixed case, with punctuation) | | `speaker_id` | int32 | 1–4 | | `style` | string | one of: `default`, `confused`, `enunciated`, `happy`, `laughing`, `narration`, `sad`, `whisper` | | `substyle` | string | finer-grained label, e.g. `default_emphasis`, `default_essentials`, `default_longform`, `narration_longform` | | `corpus` | string | `base` (short utterances) or `longform` (multi-minute readings) | | `start_s` | float32 | null for full-file rows; chunk start for longform | | `end_s` | float32 | null for full-file rows; chunk end for longform | ### Splits We follow the official Expresso train/dev/test splits, with **one TTS-oriented deviation**: - **base read** (~11,600 utterances): full-file rows, no slicing — official splits applied as-is. - **longform read** (8 source files: `default_longform`, `narration_longform` × 4 speakers): kept as **full files in `train` only**. The official Expresso splits slice each longform file into 3 non-overlapping chunks (60 s for dev/test, the rest for train) for resynthesis benchmarking. Those chunks don't align with the full-file transcripts, so for TTS/ASR we keep the longform audio + transcript intact and place the full files in `train` only. If you need the official chunked benchmark, see `original_metadata/splits/`. - **singing** is intentionally **excluded** (only 12 wavs total, not in official splits). All rows have aligned `(audio, text)` pairs. ### Style coverage per speaker All 4 speakers have all 8 styles, with these caveats: - `narration` is **longform-only** for all speakers (1 file each). - `default` includes the substyles `default`, `default_emphasis`, `default_essentials`, `default_longform`. --- ## `conversational` config ~15.9k per-utterance mono turns derived from the official 339 stereo dialog files. Each row is **one speaker's turn** at a known time range within the source file, transcribed by Whisper. | | train | dev | test | |---|---|---|---| | rows | ~14.8k | ~520 | ~515 | | audio | ~29 h | ~50 min | ~51 min | ### Schema | Column | Type | Notes | |---|---|---| | `id` | string | e.g. `ex01-ex02_default_001__ch1_23.88-28.14` | | `audio` | Audio @ 48 kHz mono | the VAD-extracted turn from one channel | | `text` | string | Whisper Large V3 Turbo transcript (mixed case + punctuation) | | `speaker_id` | int32 | this channel's speaker (1–4) | | `style` | string | this channel's expressive style | | `other_speaker_id` | int32 | partner's speaker id | | `other_style` | string | partner's expressive style | | `source_file_id` | string | e.g. `ex01-ex02_default_001` (the stereo source) | | `channel` | int32 | 1 or 2 | | `start_s` | float32 | turn start within source file (after VAD ∩ split clip) | | `end_s` | float32 | turn end | ### How it was built 1. **Parse** the official `splits/{train,dev,test}.txt` time-window assignments per source file. 2. **Intersect** each split window with `VAD_segments.txt` (per-channel pyannote turns) — turns straddling the dev/test boundary are **clipped to the split window** so dev/test never leak into train. 3. **Slice** the stereo source file → mono channel → 48 kHz mono turn. 4. **Transcribe** with `openai/whisper-large-v3-turbo`, with anti-hallucination decoding (`no_repeat_ngram_size=4`, `repetition_penalty=1.2`, `condition_on_prev_tokens=False`) and pre-resampled to 16 kHz. ### Turn filtering - **Min duration**: 0.3 s. Sub-300ms VAD turns (mostly backchannels and clicks) are dropped. - **Max duration**: 28 s. Long turns are split into ≤28 s pieces (Whisper's context is 30 s). ### Style coverage 26 styles total in the conversational subset, including styles **not present in `read`**: `angry`, `animal`, `awe`, `bored`, `calm`, `desire`, `disgusted`, `fast`, `fearful`, `nonverbal`, `projected`, `sarcastic`, `sleepy`, `sympathetic`, plus mixed pairs like `animal-animaldir` and `child-childdir` (where the two channels carry different styles — one row's `style` and `other_style` will differ). ### ASR quality (validated against `read` ground truth) We benchmarked Whisper Large V3 Turbo on 210 human-transcribed read utterances spanning all 7 transcribed read styles. Per-style WER: | confused | default | sad | happy | enunciated | laughing | whisper | **overall** | |---|---|---|---|---|---|---|---| | 0.96% | 1.67% | 2.00% | 2.76% | 3.18% | 4.98% | 5.31% | **3.00%** | ASR errors are highest on `whisper` and `laughing` styles (the toughest acoustic conditions), but still under 6% WER. Conversational rows are expected to track the same per-style quality. ### Caveats - Transcripts are **machine-generated** — expect a small error rate, especially on whispered/laughing/animal-style turns. - Mixed-style pairs (`animal-animaldir`, `child-childdir`, `sad-sympathetic` and reversals) — speakers in the two channels carry different styles. Ground-truth styles are encoded per-row in `style` (this channel) and `other_style` (partner). --- ## Sidecar files The original FAIR metadata is uploaded under `original_metadata/`: - `original_metadata/README.txt`, `LICENSE.txt` — official Expresso documentation - `original_metadata/read_transcriptions.txt` — per-file transcripts (tab-separated) - `original_metadata/VAD_segments.txt` — per-channel VAD timings for the conversational subset (used to derive the `conversational` config) - `original_metadata/splits/{train,dev,test}.txt`, `splits/README` — official split definitions ## Quick start ```python from datasets import load_dataset # Pick a config — there is no default read = load_dataset("shangeth/expresso", "read", split="train") conv = load_dataset("shangeth/expresso", "conversational", split="train") ex = read[0] print(ex["id"], "|", ex["style"], "|", ex["text"]) print(ex["audio"]["array"].shape, "@", ex["audio"]["sampling_rate"], "Hz") # Filter conv to mixed-style pairs (cross-style modeling) mixed = conv.filter(lambda x: x["style"] != x["other_style"]) print(f"{len(mixed)} cross-style turns") # Per-style coverage from collections import Counter print(Counter(conv["style"]).most_common(10)) ``` ## Reproducing this dataset ```bash # Download the official Expresso tar (~36 GB) and extract: mkdir -p data && cd data curl -L https://dl.fbaipublicfiles.com/textless_nlp/expresso/data/expresso.tar | tar -xf - cd .. # Build + push: python expresso_audio.py --repo_id shangeth/expresso --private # read config python expresso_conversational.py --repo_id shangeth/expresso --private # conversational config ``` See [github.com/shangeth/wren-datasets](https://github.com/shangeth/wren-datasets) for the full extraction code. ## Citation ```bibtex @inproceedings{nguyen2023expresso, title = {Expresso: A Benchmark and Analysis of Discrete Expressive Speech Resynthesis}, author = {Nguyen, Tu Anh and Hsu, Wei-Ning and D'Avirro, Antony and Shi, Bowen and Gat, Itai and Fazel-Zarani, Maryam and Remez, Tal and Copet, Jade and Synnaeve, Gabriel and Hassid, Michael and Kreuk, Felix and Adi, Yossi and Dupoux, Emmanuel}, booktitle = {Interspeech}, year = {2023} } @misc{wren2026, title = {Wren: A Family of Small Open-Weight Models for Unified Speech-Text Modelling}, author = {Shangeth Rajaa}, year = {2026}, url = {https://github.com/shangeth/wren} } ``` ## License **CC-BY-NC-4.0** — non-commercial use only. See `original_metadata/LICENSE.txt`.