Feature Extraction
Transformers
Safetensors
PyTorch
Russian
accentik
russian
accentuation
word-stress
custom-code
custom_code
Instructions to use TeraSpace/accentik with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TeraSpace/accentik with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="TeraSpace/accentik", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TeraSpace/accentik", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 6,359 Bytes
40f546e daad4bf 40f546e daad4bf 40f546e daad4bf 40f546e daad4bf 40f546e daad4bf 40f546e daad4bf 40f546e daad4bf 40f546e daad4bf 40f546e daad4bf 40f546e daad4bf 40f546e daad4bf 40f546e daad4bf 40f546e daad4bf 40f546e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | {
"release": "audio-v3",
"reference_release": "experimental-context-v2",
"reference_weights_sha256": "8e0be4ecb637a35c4f152e2cb887bca8e95ab1f1b17daa4f62a1706331f7aa3e",
"source_checkpoint_sha256": "f54918eb8146ee85fa429f1dffe30cf8c2adce3132b057fe2d9adc35b1f311ba",
"stress_checkpoint_sha256": "5342342f6469de3f985dbc0799a3083f2ed8f979488f920812fb6a911125bd54",
"stress_weights_bitwise_preserved": true,
"input_yo_hidden_for_paired_comparison": true,
"stress_checks": {
"RHD_manual": {
"label_kind": "manual contextual reference",
"targets": 7676,
"context_v2_correct": 7482,
"audio_v3_correct": 7484,
"context_v2_score": 0.9747264200104221,
"audio_v3_score": 0.9749869723814487,
"context_v2_macro": 0.9637496548122184,
"audio_v3_macro": 0.9679068553070598
},
"external_russtress": {
"label_kind": "public reference, previously reused during development",
"targets": 11089,
"context_v2_correct": 10435,
"audio_v3_correct": 10483,
"context_v2_score": 0.9410226350437371,
"audio_v3_score": 0.9453512489854811,
"context_v2_macro": 0.9361600330721678,
"audio_v3_macro": 0.9425269193674681
},
"TeraTTS/stress_dataset_sft_proza": {
"label_kind": "supplied labels",
"targets": 21234,
"context_v2_correct": 19900,
"audio_v3_correct": 19913,
"context_v2_score": 0.9371762268060657,
"audio_v3_score": 0.9377884524818687,
"context_v2_macro": 0.8681086799998965,
"audio_v3_macro": 0.8710729274781607
},
"TeraTTS/open_accent": {
"label_kind": "supplied labels",
"targets": 46994,
"context_v2_correct": 43577,
"audio_v3_correct": 44045,
"context_v2_score": 0.9272885900327701,
"audio_v3_score": 0.9372473081670001,
"context_v2_macro": 0.8973879348784138,
"audio_v3_macro": 0.9089840690228318
},
"TeraTTS/stress_dataset_sft_poetry": {
"label_kind": "supplied labels",
"targets": 5882,
"context_v2_correct": 4981,
"audio_v3_correct": 4995,
"context_v2_score": 0.846820809248555,
"audio_v3_score": 0.8492009520571234,
"context_v2_macro": 0.811748154183098,
"audio_v3_macro": 0.8215229111497144
},
"supplied_dialogue": {
"label_kind": "RUAccent pseudo-label agreement",
"targets": 42965,
"context_v2_correct": 42483,
"audio_v3_correct": 42685,
"context_v2_score": 0.9887815663912487,
"audio_v3_score": 0.9934830676131735,
"context_v2_macro": 0.9561116028270102,
"audio_v3_macro": 0.9954060892784302
},
"agent_authored": {
"label_kind": "authored development diagnostics; not representative",
"targets": 12,
"context_v2_correct": 12,
"audio_v3_correct": 7,
"context_v2_score": 1.0,
"audio_v3_score": 0.5833333333333334,
"context_v2_macro": 1.0,
"audio_v3_macro": 0.5833333333333334
},
"audio/portal_ru": {
"label_kind": "filtered acoustic teacher agreement",
"targets": 58,
"context_v2_correct": 58,
"audio_v3_correct": 57,
"context_v2_score": 1.0,
"audio_v3_score": 0.9827586206896551,
"context_v2_macro": 1.0,
"audio_v3_macro": 0.9827586206896551
}
},
"manual_context_macro": {
"context_v2": 0.9637496548122184,
"audio_v3": 0.9679068553070598
},
"acoustic_teacher_agreement": {
"context_v2": 0.9921076879932695,
"audio_v3": 0.9965546252153359
},
"yo": {
"experimental": true,
"enabled_by_default": false,
"validation_selected_threshold": 0.8,
"known_manual_targets": {
"tp": 736,
"fp": 34,
"fn": 49,
"tn": 2818,
"precision": 0.9558441558441558,
"recall": 0.9375796178343949,
"f1": 0.9466237942122186,
"accuracy": 0.9771789936761067
},
"authored_target_diagnostic": {
"correct": 39,
"total": 45,
"positive_correct": 24,
"positive_total": 27,
"negative_correct": 15,
"negative_total": 18
},
"authored_whole_text_diagnostic": {
"sentences": 45,
"whole_sentence_correct": 36,
"tp": 28,
"fp": 6,
"fn": 6,
"precision": 0.8235294117647058,
"recall": 0.8235294117647058
},
"diagnostic_representative": false,
"passed_default_restoration_gate": false
},
"data": {
"train_contexts": 472192,
"validation_contexts": 54434,
"test_contexts": 67048,
"usable_audio_texts": 49996,
"filtered_acoustic_word_targets": 323918,
"unknown_labels_masked": true,
"source_texts_redistributed": false
},
"label_provenance": {
"clarified_on": "2026-10-28",
"supplied_dialogue": {
"stress_label_generator": "RUAccent",
"provenance_evidence": "User confirmed that the Russian dialogue data was stressed by RUAccent.",
"metric_kind": "RUAccent pseudo-label agreement",
"independent_human_reference": false,
"interpretation": "An increase measures closer agreement with RUAccent, including its errors. Held-out contexts do not make automatic labels an independent correctness reference.",
"numeric_scores_changed": false
},
"audio_ru_dialogs": {
"metric_kind": "Filtered TeraAnaly acoustic-teacher agreement",
"teacher_training_caveat": "TeraAnaly used RUAccent-derived training labels.",
"independent_human_reference": false
},
"reference_comparisons": {
"sources": [
"RHD_manual",
"external_russtress"
],
"interpretation": "Report separately from pseudo-label agreement. Public references have been reused during development and do not establish a score on the private benchmark."
}
},
"limitations": [
"Dialogue labels were generated by RUAccent; agreement includes teacher errors.",
"Acoustic scores measure agreement with TeraAnaly, itself trained with RUAccent-derived labels.",
"Public reference tests were reused during development; historical model/data overlap is unknown.",
"Authored diagnostics are development regression checks, not a representative human benchmark.",
"Optional ё placement can invent or miss ё and can disagree with predicted stress.",
"No private benchmark examples or expected answers were used; no claim of beating RUAccent."
]
}
|