File size: 4,418 Bytes
bde64fc 06b9041 bde64fc 06b9041 bde64fc 06b9041 e0f53e0 06b9041 e0f53e0 06b9041 e0f53e0 06b9041 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | ---
language:
- en
license: apache-2.0
tags:
- token-classification
- prosody
- tobi
- speech
- linguistics
- distilbert
base_model: distilbert-base-uncased
datasets:
- LibriTTS
- peoples_speech
- SBCSAE
---
# text2tobi — `libri+peoples+sbc`
ToBI prosodic annotation from text alone. No audio required at inference time.
Given a stream of lowercased words, this model predicts:
- **Intonation unit boundaries** — where a prosodic phrase ends
- **Intonation direction** at each boundary — rising (H%), falling (L%), or level (!H%)
- **Break index strength** at each boundary — intermediate (3) or full (4)
This is the `libri+peoples+sbc` checkpoint: the best-performing configuration from the Text2ToBI experiments, trained on LibriTTS + People's Speech + SBCSAE with boundary loss weight 2.0, no POS injection, punctuation stripped.
---
## Usage
The recommended way to use this model is via the [text2tobi CLI](https://github.com/Howell-Prosody-Lab/Text2ToBI):
```bash
pip install torch transformers huggingface_hub
git clone https://github.com/Howell-Prosody-Lab/Text2ToBI
cd text2tobi
python -m text2tobi download
python -m text2tobi "the students filed into the lecture hall"
```
Example output (default table format):
```
word boundary intonation break_index
the - - -
students - - -
filed - - -
into - - -
the - - -
lecture - - -
hall B L% 4
```
Pass `--raw` for inline annotations or `--ssml` for SSML XML output.
### Loading directly
If you want to load the model without the CLI, include `model.py` from this repo in your working directory:
```python
from model import ProsodyBoundaryModel
from transformers import AutoTokenizer
model = ProsodyBoundaryModel.from_pretrained("primsune/text2tobi")
tokenizer = AutoTokenizer.from_pretrained("primsune/text2tobi")
model.eval()
```
The model returns a dict of logits keyed `boundary_logits`, `intonation_logits`, and `break_idx_logits`.
---
## Performance
Evaluated on SBC001–005 (held-out test set, never seen during training). This is the only configuration directly comparable to the GPT-Neo text-only baseline from Roll et al. (2023).
| Model | Boundary F1 | Intonation F1 | Break Index F1 |
|---|---|---|---|
| **text2tobi** `libri+peoples+sbc` BLW=2.0 | **0.8352** | **0.5765** | 0.6018† |
| GPT-Neo 1.2B (Roll et al., 2023) | 0.770 | — | — |
| Random (distribution-matched) | 0.257 | — | — |
†Break index F1 is evaluated on BU Radio News Corpus gold `.brk` annotations (not the SBC test set, which has no break index labels). Treat as experimental.
text2tobi surpasses the GPT-Neo baseline by 6.5 points while being approximately 18× smaller (~66M vs ~1.2B parameters), and without access to punctuation or capitalization — input is lowercased words only.
---
## Training data
| Corpus | Annotation | Role |
|---|---|---|
| LibriTTS | Silver (PSST + Wav2ToBI consensus) | Boundary + intonation |
| People's Speech | Silver (PSST + Wav2ToBI consensus) | Boundary + intonation |
| SBCSAE | Gold (Du Bois transcripts) | Boundary + intonation |
| BU Radio News | Gold (`.brk` files) | Break index evaluation only |
Silver-standard boundary and intonation labels were generated by cross-validating PSST (`NathanRoll/psst-medium-en`) against Wav2ToBI (`ReginaZ/Wav2ToBI-PB-Fuzzy`). Positions where the two systems disagreed were masked from training. 87.3% of utterance-final words received Wav2ToBI corroboration within ±1 word.
SBCSAE data is included under explicit written permission from corpus director John W. Du Bois (June 2026) for unrestricted public distribution of derived model weights.
---
## Known limitations
- **Intonation labels apply to boundary words only.** Non-boundary intonation is not modeled.
- **Register coverage** is read speech (LibriTTS, People's Speech) and conversational speech (SBCSAE). Generalization to telephony, noisy environments, or non-native speakers has not been tested.
- **Chunking fallback**: for unpunctuated input, the inference pipeline splits at a 100-token word boundary when no sentence boundary is detected. This is not linguistically motivated and may affect predictions near split points.
---
## License
Apache 2.0. See [LICENSE](LICENSE).
|