diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..759659ea43362a7b1a69ff90278ff7e361667955 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,31 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +assets/architecture.png filter=lfs diff=lfs merge=lfs -text +assets/indicemo-overall.png filter=lfs diff=lfs merge=lfs -text +assets/indicemo-by-delivery.png filter=lfs diff=lfs merge=lfs -text +assets/nova-results.png filter=lfs diff=lfs merge=lfs -text +samples/ira-professional-steady.wav filter=lfs diff=lfs merge=lfs -text +samples/ira-sad-slow.wav filter=lfs diff=lfs merge=lfs -text +samples/ira.wav filter=lfs diff=lfs merge=lfs -text +samples/aisha.wav filter=lfs diff=lfs merge=lfs -text +samples/siya.wav filter=lfs diff=lfs merge=lfs -text +samples/zoya.wav filter=lfs diff=lfs merge=lfs -text +assets/nova-detector-breakdown.png filter=lfs diff=lfs merge=lfs -text +assets/rumik-capabilities.png filter=lfs diff=lfs merge=lfs -text +assets/cer-selected.png filter=lfs diff=lfs merge=lfs -text +assets/cer-full.png filter=lfs diff=lfs merge=lfs -text +samples/showcase/01-the-second-cup.wav filter=lfs diff=lfs merge=lfs -text +samples/showcase/02-one-last-message.wav filter=lfs diff=lfs merge=lfs -text +samples/showcase/03-we-made-it.wav filter=lfs diff=lfs merge=lfs -text +samples/showcase/04-a-broken-promise.wav filter=lfs diff=lfs merge=lfs -text +samples/showcase/05-ready-for-departure.wav filter=lfs diff=lfs merge=lfs -text +samples/showcase/06-home-again.wav filter=lfs diff=lfs merge=lfs -text +samples/showcase/07-a-place-to-read.wav filter=lfs diff=lfs merge=lfs -text +samples/showcase/08-the-acceptance-letter.wav filter=lfs diff=lfs merge=lfs -text +samples/showcase/09-a-small-celebration.wav filter=lfs diff=lfs merge=lfs -text +samples/showcase/10-the-demo-is-ready.wav filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text +assets/wer-selected.png filter=lfs diff=lfs merge=lfs -text +assets/wer-full.png filter=lfs diff=lfs merge=lfs -text +assets/rumik-logo.png filter=lfs diff=lfs merge=lfs -text diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..65990a8910c518b3bb8d8f0689bcf12d2939f0cd --- /dev/null +++ b/LICENSE @@ -0,0 +1,430 @@ +rumik-oss 1 +Copyright 2026 Rumik AI + +rumik-oss 1 is a derivative of CohereLabs/tiny-aya-fire and is released under +the Creative Commons Attribution-NonCommercial 4.0 International license +(CC-BY-NC 4.0), together with the Cohere Labs acceptable-use addendum that +governs the upstream model. + + License ......... https://cohere.com/cohere-labs-cc-by-nc-license + Acceptable use .. https://docs.cohere.com/docs/cohere-labs-acceptable-use-policy + +Use of this model is permitted for research and other non-commercial purposes +only, and must comply with the Cohere Labs Acceptable Use Policy in addition +to the terms below. 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CC-BY-NC 4.0 requires that redistribution +indicate what was changed relative to the upstream material; this file records +those changes. + +----------------------------------------------------------------------- +Lineage: + + CohereLabs/tiny-aya-fire vocab 261,008 + https://huggingface.co/CohereLabs/tiny-aya-fire + Licensed CC-BY-NC 4.0 with the Cohere Labs acceptable-use addendum. + + -> rumik-ai/rumik-oss-1-base vocab 277,395 + https://huggingface.co/rumik-ai/rumik-oss-1-base + Named-speaker text-to-speech training. + + -> rumik-ai/rumik-oss-1 vocab 277,404 (this model) + Delivery-control fine-tuning and reinforcement learning. +----------------------------------------------------------------------- + +Modifications made by Rumik AI, relative to tiny aya fire: + + 1. Vocabulary extended from 261,008 to 277,404 tokens: + - 16,384 Mimi codec unit tokens _, ids 261008-277391 + - 3 structure delimiters , , ids 277392-277394 + - 9 delivery and vocalization tags, ids 277395-277403 + The first two groups were added for rumik-oss 1 base; the tags were + added for rumik-oss 1. Input and output embedding matrices were + resized accordingly. + + 2. A stop-prediction head (`stop_predictor`) was added on top of the final + hidden state to terminate audio generation. + + 3. The model was post-trained for text-to-speech on multilingual Indic and + English speech data, followed by a reinforcement-learning stage. + + 4. Model class and configuration were reimplemented as RumikOSSForCausalLM / + RumikOSSConfig (model_type "rumik_oss"), subclassing the transformers + Cohere2 implementation. + + 5. The inherited chat template was removed. This is not a chat model and + does not take a `messages` list; it uses the flat prompt layout + documented in README.md. + +----------------------------------------------------------------------- +Also bundled: + +Mimi codec (codec/) - Kyutai + https://huggingface.co/kyutai/mimi + Licensed CC-BY-4.0. Redistributed unmodified. +----------------------------------------------------------------------- diff --git a/README.md b/README.md index 32897cd3e640101ba184f8c4ccd896981de3804a..f8170e48fa3a51b73cdf7963e35e59ce82159dc9 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,391 @@ --- -license: mit +pipeline_tag: text-to-speech +library_name: transformers +base_model: rumik-ai/rumik-oss-1-base +base_model_relation: finetune +license: cc-by-nc-4.0 +language: + - hi + - bn + - ta + - te + - mr + - gu + - kn + - ml + - pa + - or + - as + - ur + - ne + - sa + - mai + - mni + - brx + - doi + - kok + - sat + - ks + - en +tags: + - tts + - text-to-speech + - indic + - expressive + - mimi + - speech --- +

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+ +

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+ +# rumik-oss 1 + +![rumik-oss 1 multilingual speech, expressive delivery, and inline vocalizations](https://huggingface.co/rumik-ai/rumik-oss-1/resolve/main/assets/rumik-capabilities.png) + +rumik-oss 1 is a **3b multilingual text-to-speech model** from rumik ai, trained on **fewer than 70,000 hours of speech** while performing competitively with existing TTS models. it combines code-switched synthesis, description-conditioned delivery, and inline vocalization control, with **24 khz audio output**. + +we also release [rumik-oss 1 base](https://huggingface.co/rumik-ai/rumik-oss-1-base), our final speaker-conditioned checkpoint before post-training. it supports multilingual synthesis with ira, aisha, siya, and zoya and provides a starting point for the community to research and post-train the base model from scratch. + +we describe the post-training of rumik-oss 1 and the decisions behind its training curriculum in our [blog](https://rumik.ai/research/rumik-oss). our **technical report** will provide an in-depth account of the architecture, data preparation, training, and evaluation. + +## model overview + +rumik-oss 1 extends tiny aya fire with discrete speech tokens from the mimi codec. following the flattened codec-token formulation used in [llama-mimi](https://arxiv.org/abs/2509.14882), text conditioning and audio generation share a single autoregressive sequence. the model predicts eight codebook tokens for each audio frame, in codebook order, before advancing to the next frame. generated tokens are regrouped into codec frames and reconstructed as a waveform by the frozen mimi decoder. + +we release the model with 4 voices: + +- Ira +- Aisha +- Siya +- Zoya + +unlike other TTS models, where each voice is trained specifically for one language or performs best in one language, our voices perform equally well across all 22 languages. + +![rumik-oss 1 synthesis architecture and frame-major token-to-codec reconstruction](https://huggingface.co/rumik-ai/rumik-oss-1/resolve/main/assets/architecture.png) + +## capabilities + +- **multilingual synthesis:** 22 indic languages in their native scripts and romanized forms, plus english, with support for single-language and code-switched synthesis. +- **delivery conditioning:** control tone, accent, and pace through the description format for various scenarios. +- **vocalization control:** inline tags for laughter, chuckles, and sighs. + +## delivery controls + +the `` prefix lets users control the global tone and pace of the audio, while inline tags let you insert ``, ``, and `` at the intended positions. + + +```text + जल्दी आओ, हमारा नाम लिस्ट में है! आज घर में जश्न होगा। +``` + +| control | values | +|---|---| +| speaker | `Ira`, `Aisha`, `Siya`, `Zoya` | +| tone | happy, sad, angry, excited, professional | +| accent | `Hindi`, `Telugu`, `Tamil`, `Kannada`, `Bengali`, `Punjabi`, `Indian English` | +| pace | slow, fast, steady | +| inline vocalization | ``, ``, `` | + +## audio samples + +**ira · hindi · sad · slow pace** + +> माँ, आज फिर तुम्हारे लिए चाय बना दी। <sigh> कप ठंडा हो गया, पर तुम्हारा इंतज़ार नहीं। + + + +**aisha · english · sad · slow pace** + +> I still play your last message, Dad. You only said, call me when you get home. I got home. I just never got to tell you. + + + +**siya · telugu · excited · fast pace** + +> అమ్మా, నాకు ఉద్యోగం వచ్చింది! నిజంగా వచ్చింది! నువ్వు నా కోసం చేసిన ప్రతి త్యాగం నాకు గుర్తుంది. <laugh> ఈ రోజు నీకు నచ్చిన స్వీట్లు నేనే కొనిస్తాను! + + + +**zoya · tamil · angry · fast pace** + +> நீ வருவேன்னு சொன்னதால்தான் இவ்வளவு நேரம் காத்திருந்தேன்! ஒரு போன் கூட பண்ண முடியலையா? ஒவ்வொரு முறையும் மன்னிப்பு கேட்டா மட்டும் எல்லாம் சரியாகிடாது! + + + +**ira · english · professional · steady pace** + +> Good evening, passengers. Boarding for the flight to Bengaluru will begin shortly at gate twelve. Please keep your boarding pass ready. Thank you for your patience, and have a pleasant journey. + + + +**aisha · bengali · happy · steady pace** + +> এত দিন পরে তোমাকে দেখে কী যে ভালো লাগছে! তোমার পছন্দের সব রান্না করেছি। আজ আর কোথাও যেতে দেব না, সবাই মিলে অনেক গল্প করব। + + + +**siya · kannada · professional · steady pace** + +> ನಮ್ಮ ಹೊಸ ಗ್ರಂಥಾಲಯಕ್ಕೆ ಸ್ವಾಗತ. ಇಲ್ಲಿ ಕನ್ನಡ ಮತ್ತು ಇಂಗ್ಲಿಷ್ ಪುಸ್ತಕಗಳು ಲಭ್ಯವಿವೆ. ಸದಸ್ಯರಾಗಲು ನಿಮ್ಮ ಹೆಸರು ಮತ್ತು ವಿಳಾಸ ನೀಡಿ. ಓದಲು ಶಾಂತವಾದ ಸ್ಥಳವೂ ಇದೆ. + + + +**zoya · punjabi · excited · fast pace** + +> ਮਾਂ, ਮੇਰਾ ਦਾਖ਼ਲਾ ਹੋ ਗਿਆ! ਸੱਚੀਂ, ਚਿੱਠੀ ਆ ਗਈ ਹੈ! ਜਿਹੜਾ ਸੁਪਨਾ ਅਸੀਂ ਇਕੱਠੇ ਵੇਖਿਆ ਸੀ, ਉਹ ਅੱਜ ਪੂਰਾ ਹੋ ਗਿਆ। ਹੁਣ ਸਭ ਨੂੰ ਫ਼ੋਨ ਕਰ! + + + +**ira · hindi + english · happy · steady pace** + +> आज की meeting में सबको हमारा idea पसंद आया। I was so nervous, लेकिन तुमने कहा था ना, बस दिल से बोलो। <chuckle> अब coffee मेरी तरफ़ से, और cake तुम्हारी तरफ़ से! + + + +**aisha · telugu + english · professional · steady pace** + +> మన కొత్త demo సిద్ధంగా ఉంది. You can choose a voice, change the pace, and try different languages. ముందుగా తెలుగులో ఒక వాక్యం విందాం, then we can switch to English. + + + +
+generation settings + +temperature `0.8`, top-k `30`, top-p `1.0`, maximum `3072` tokens. [prompts and seeds](samples/showcase/manifest.json). + +
+ +## benchmarks + +to evaluate our specific use case, we need new benchmarks across different axes: expressiveness, consistency of non-verbal vocalizations, and basic WER/CER. there aren't any standardized benchmarks for multilingual indic TTS across these axes, so we present 3 benchmarks: + +- IndicEmo: evaluates expressiveness. +- NoVA: evaluates adherence to non-verbal vocalization requests. +- WER/CER: evaluates word and character error rates across 15 languages. + + + +### IndicEmo + +[github](https://github.com/ira-rumik/IndicEmo) + +we introduce IndicEmo to evaluate expressive delivery in code-switched speech. its 100 prompts combine two to five languages drawn from english, hindi, telugu, tamil, kannada, bengali, and punjabi, with equal coverage of happy, sad, angry, excited, and professional delivery. + +three automated judges rate anonymized recordings on a five-point rubric covering tone, dynamics, phrasing, and sustained expression. results use the 98-prompt intersection with valid ratings from all judges, aggregating the median rating per recording into category means. rumik-oss 1 scores **2.92/5 overall** and **3.03/5 on the four emotion categories**. + +![IndicEmo overall expression quality across five tested tts configurations](https://huggingface.co/rumik-ai/rumik-oss-1/resolve/main/assets/indicemo-overall.png) + +
+scores and emotion breakdown + +| system | overall / 5 | emotions only / 5 | +|---|---:|---:| +| gemini 3.1 flash tts preview | 4.58 | 4.66 | +| **rumik-oss 1** | **2.92** | **3.03** | +| cartesia sonic preview | 2.71 | 2.51 | +| cartesia sonic 3.5 | 2.32 | 2.18 | +| elevenlabs eleven v3 | 2.16 | 2.03 | + +![IndicEmo scores for happy, sad, angry, excited, and professional](https://huggingface.co/rumik-ai/rumik-oss-1/resolve/main/assets/indicemo-by-delivery.png) + +the emotions-only score excludes professional delivery. [evaluation protocol](benchmarks/evaluation.md#indicemo). + +
+ +### NoVA + +[github](https://github.com/ira-rumik/nova-bench) + +NoVA evaluates adherence to inline laughter, chuckle, and sigh requests in english speech. each of its 149 prompts places a single vocalization at a specified word position. seven systems are evaluated over three synthesis runs using provider-specific prompting. + +silk-asr, scribe v2, and a gemini verifier assess position-correct rendering. laugh and chuckle are pooled into one category; the final score averages laughter and sigh rendering rates across detectors, with unsupported categories contributing zero. rumik-oss 1 achieves a **0.884 rendering score**. + +![NoVA rendering scores across seven tested tts configurations](https://huggingface.co/rumik-ai/rumik-oss-1/resolve/main/assets/nova-results.png) + +
+scores and detector breakdown + +| system (voice) | rendering score | +|---|---:| +| grok tts (ara) | 0.972 | +| inworld tts-2 (ashley) | 0.941 | +| **rumik-oss 1 (ira)** | **0.884** | +| orpheus 3b (tara) | 0.813 | +| elevenlabs v3 (jessica) | 0.705 | +| gemini 3.1 flash tts (kore) | 0.664 | +| cartesia sonic 3.6 (monica) | 0.498 | + +![NoVA position-correct vocalization rendering by detector](https://huggingface.co/rumik-ai/rumik-oss-1/resolve/main/assets/nova-detector-breakdown.png) + +[evaluation protocol](benchmarks/evaluation.md#nova). + +
+ +### WER / CER + +[github](https://github.com/Demon-Sheriff/rumik-oss-bench) + +we evaluate transcription fidelity using IndicConformer RNNT, comparing normalized ASR transcripts against the input text. the tables report WER and CER across 15 languages. we release the benchmark prompts, generation and scoring code, and per-sample results in [rumik-oss-bench](https://github.com/Demon-Sheriff/rumik-oss-bench), with normalization and scoring details in the [methodology](https://github.com/Demon-Sheriff/rumik-oss-bench/blob/main/METHODOLOGY.md). + +#### WER (%) + +![WER language comparison](https://huggingface.co/rumik-ai/rumik-oss-1/resolve/main/assets/wer-selected.png) + +
+all 15 languages · WER + +![WER: complete 15-language table](https://huggingface.co/rumik-ai/rumik-oss-1/resolve/main/assets/wer-full.png) + +[results](benchmarks/wer-reported-scores.json) · [evaluation protocol](benchmarks/evaluation.md#wer-and-cer) + +
+ +#### CER (%) + +![CER language comparison](https://huggingface.co/rumik-ai/rumik-oss-1/resolve/main/assets/cer-selected.png) + +
+all 15 languages · CER + +![CER: complete 15-language table](https://huggingface.co/rumik-ai/rumik-oss-1/resolve/main/assets/cer-full.png) + +[results](benchmarks/cer-reported-scores.json) · [evaluation protocol](benchmarks/evaluation.md#wer-and-cer) + +
+ +## limitations + +- **long-form synthesis:** training utterances are limited to 30 seconds; speaker and prosodic continuity over longer passages have not been evaluated. we do not recommend generating utterances longer than 35 seconds. +- **vocalization precision:** NoVA detectors report unrequested vocalizations in 2.9-5.4% of rumik-oss 1 outputs. NoVA evaluates english; equivalent tag adherence across languages has not been established. + +## inference + +rumik-oss 1 generates **audio tokens**. decoding them to a waveform is a +separate step that uses the [mimi](https://huggingface.co/kyutai/mimi) codec, +bundled in this repository under `codec/`. + +`transformers.pipeline("text-to-speech")` is not supported for this model. +use the [one-shot example](#one-shot-example) to generate audio tokens with +`generate_audio()` and decode them into a waveform with mimi. + +### installation + +```bash +pip install -r requirements.txt +``` + +requires an nvidia gpu with cuda support. the one-shot example below also +needs `soundfile` to write a wav. `requirements-server.txt` adds the extras +needed only for the optional web ui. + +### one-shot example + +text in, `speech.wav` out: + +```python +"""rumik-oss 1: text -> speech.wav""" +import soundfile as sf +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer, MimiModel + +REPO = "rumik-ai/rumik-oss-1" +SPEAKER = "Ira" +DESCRIPTION = "happy, Hindi accent, steady pace" +TEXT = "नमस्ते, आज आपका दिन कैसा रहा?" + +tokenizer = AutoTokenizer.from_pretrained(REPO, trust_remote_code=True) +model = AutoModelForCausalLM.from_pretrained( + REPO, trust_remote_code=True, dtype=torch.bfloat16 +).eval().cuda() +mimi = MimiModel.from_pretrained(REPO, subfolder="codec").eval().cuda() + +# [BOS] Ira: text