Modularcomputing/dynamic-accent-preview

Dynamic Accent: Code-switching TTS

Code-switching TTS for Nigerian languages. English, Nigerian Pidgin, Igbo, Yoruba and Hausa, mixed freely inside one sentence and spoken by one consistent voice, with no model swapping between languages.

New: Dynamic Accent Flash (--version flash, now the default) is the same recipe ported to FunAudioLLM/Fun-CosyVoice3-0.5B-2512, then preference-tuned with DPO for naturalness, intelligibility, pace and pauses, with a text front-end that restores Yoruba/Igbo diacritics. It is trained on licensed data only. See Flash results below.

Dynamic Accent (Phase 2/3) is a fine-tune of FunAudioLLM/CosyVoice2-0.5B by Modular. It adds:

• per-token language embeddings, a speaker token and an adversarial (gradient-reversal) speaker classifier on the LM, so the voice stays the same across a language switch (after Zhang et al., Google, 2019, Learning to Speak Fluently in a Foreign Language);
• training on about 178 h of Nigerian-language and code-switched speech, including about 102 h of Igbo;
• learned [pause]/[breath] events and a speaking-rate instruction (Phase 3);
• a flow-matching decoder fine-tuned on the anchor voice for tone and pitch detail.

It is a research release, not a production system. Read Limitations before you use it.

Versions and voices

Three model versions ship side by side. Pick one with --version, or render several with --compare and A/B before use.

version	LM	flow	decoding	what changed
flash (default)	checkpoints/flash/flash_dpo on Fun-CosyVoice3-0.5B-2512: Flash SFT (CV3 LM, the Phase 3 data re-weighted towards natural speech, plus IgboSynCorp pseudo-transcripts) + DPO (1,206 preference pairs; reward = naturalness + CER + articulation rate + pause rate)	stock CV3 flow + vocoder, zero-shot from the voice clip	text front-end (numbers verbalised, missing Yoruba/Igbo diacritics restored by checkpoints/flash/diacritics_byt5), one pass per paragraph, silence-aware repetition check, no audio post-processing	pace about native, fewest clipped endings for every voice, lower anchor CER (see Flash results)
phase3	checkpoints/phase3/p3_lm_full (Phase 2 LM + 5,000 steps on a 178 h re-cut with ~102 h Igbo, [pause]/[breath] tags and a speed instruction, rate normal)	shared checkpoints/phase2/p2_flow_sft	one pass per paragraph, silence-aware repetition check, no audio post-processing	lower CER in Igbo/Hausa/Yoruba/code-switch/paragraphs, comma pauses at the anchor's length, clipped endings 63% → 11%
phase2	checkpoints/phase2/p2_lm_B (~120 h, pause profile B)	checkpoints/phase2/p2_flow_sft	sentence by sentence, internal silences capped at 0.6 s	the Phase 2 final system

voice (--voice)	who	notes
anchor_male (default)	WAXAL Yoruba speaker 4, male	the voice both versions were trained around
waxal_ibo_5_female	WAXAL Igbo speaker 5, female, native Igbo	zero-shot (prompt clip, no fine-tune); best CER of the three on the female test subset
waxal_yor_5_female	WAXAL Yoruba speaker 5, female, native Yoruba	zero-shot; strongest identity match, but its 3.8 s prompt has no pauses, so pauses are short and endings often clipped

Recommended: flash with any of the three voices; it scores best overall (see Flash results), with one known weakness: with anchor_male it under-pauses in Igbo and Yoruba. Earlier listening verdict (before Flash): Phase 3 sounds best with the female voices, Phase 2 is fine for the male anchor. infer.py prints a note when you pick a pairing outside these. The female voices run with phase2 but are untested there. Settings live in settings.json.

Setup (single GPU)

# 1. CosyVoice code (the commit the model was built and tested on)
git clone --recursive https://github.com/FunAudioLLM/CosyVoice.git
cd CosyVoice && git checkout 074ca6dc9e80a2f424f1f74b48bdd7d3fea531cc && git submodule update --init --recursive && cd ..

# 2. This repo
huggingface-cli download Modularcomputing/dynamic-accent-preview --local-dir dynamic-accent-preview

# 3. Python deps (Python 3.10; on Colab keep the preinstalled torch/torchaudio)
pip install -r dynamic-accent-preview/requirements.txt

The stock base models download on first run and need no changes: FunAudioLLM/Fun-CosyVoice3-0.5B-2512 to pretrained/Fun-CosyVoice3-0.5B-2512 for flash (--cv3_dir / CV3_DIR to reuse a copy), and FunAudioLLM/CosyVoice2-0.5B to pretrained/CosyVoice2-0.5B for phase2/phase3. The same CosyVoice commit runs both (it has CosyVoice3). flash needs a CUDA GPU (tested on an H100, bf16). Point --model_dir (or COSYVOICE2_DIR) at an existing copy, and --cosyvoice_dir (or COSYVOICE_REPO) at your CosyVoice clone. A 16 GB GPU is enough for the CLI; the Gradio app keeps both versions loaded. Tested on an H100 (bf16). infer.py also runs on CPU in fp32, which is slow. T4-class GPUs fall back to fp16, which has not been tested.

Usage

cd dynamic-accent-preview
# default: --version flash --voice anchor_male
python infer.py --cosyvoice_dir ../CosyVoice --text "<eng>this is dynamic accent,</eng> <pcm>e dey sound nice abi?</pcm>" --out out.wav

# Flash with the female Igbo voice
python infer.py --cosyvoice_dir ../CosyVoice --version flash --voice waxal_ibo_5_female \
  --text "<ibo>Kedu ka ị mere?</ibo> <pcm>Abeg no vex, I go call you for evening.</pcm>" --out ibo_f.wav

# plain Yoruba without tone marks: Flash's front-end restores them before synthesis
python infer.py --cosyvoice_dir ../CosyVoice --version flash --voice waxal_yor_5_female --frontend native --frontend_lang yor \
  --text "E kaaaro, se daadaa ni gbogbo yin wa?" --out yor.wav

# A/B: the same text, seed and voice through several versions -> cmp_phase2.wav, cmp_phase3.wav, cmp_flash.wav
python infer.py --cosyvoice_dir ../CosyVoice --compare --compare_versions phase2,phase3,flash --voice anchor_male --text "..." --out cmp.wav

python infer.py --list_voices     # voices, versions, recommended pairing

flag	what it does
--text	the text: tagged (below) or plain with --auto_tag
--version flash|phase3|phase2	model version (default flash)
--voice anchor_male|waxal_ibo_5_female|waxal_yor_5_female	voice (default anchor_male); old ids anchor, waxal_ibo_5, waxal_yor_5 still work
--list_voices	list voices, versions and the recommended pairing
--compare, --compare_versions phase2,phase3,flash	render with several versions → <out>_<version>.wav
--frontend codeswitch|native|accent	flash text front-end: codeswitch (default; tagged text, or untagged text in --frontend_lang), native (whole text in --frontend_lang, diacritics restored), accent (words as written, read with the --frontend_lang accent)
--frontend_lang eng|pcm|ibo|yor|hau	language for untagged text / native / accent
--no_diacritics	flash: do not restore missing Yoruba/Igbo tone marks and underdots
--frontend translate --experimental_translate	experimental, off by default: translate English/Pidgin into --frontend_lang (ibo/yor/hau) with MADLAD-400-3B-MT (Apache-2.0, ~12 GB download) before synthesis. MADLAD often loops (repeated words or dots) on longer inputs and translates Pidgin poorly, so check the printed text
--cv3_dir	flash: path of the Fun-CosyVoice3-0.5B-2512 snapshot
--rate default|none|very slow|slow|normal|fast|very fast	Phase 3 / Flash speaking-rate instruction to the LM (default: the version's setting, normal). It is a learned instruction, not time-stretching. phase2 ignores it
--auto_tag	tag untagged mixed text automatically (see below), print the tagged text, then synthesise
--tag_only	with --auto_tag: print the tagged text and stop, so you can edit it and pass it back as --text
--force_lang eng|pcm|ibo|yor|hau	read the whole text in one language (tags are ignored)
--lang_map "omo=pcm,sha=pcm"	per-word language overrides for the tagger
--lid none|glotlid	with --auto_tag: add GlotLID v3 clause scores (Apache-2.0 plus notices, 1.7 GB, pip install fasttext)
--seed N	sampling seed (default 0). Try 2–3 seeds and keep the take you like
--out file.wav	output path (24 kHz WAV)
--dtype auto|bf16|fp16|fp32	LM autocast dtype on GPU (samples: bf16). CPU always runs fp32
--stitch	phase3: sentence by sentence instead of one pass (not recommended)
--prompt_clip 0-3	which anchor prompt clip (released systems: 0)
--model_dir, --cosyvoice_dir, --lm, --flow, --prompts, --voices	paths and checkpoint overrides
--pause_mode clause, --speed	phase2 only: the experimental "prosody v2" (fixed gaps plus WSOLA speed-up). A listening test rejected it; the defaults (model, 1.0) are the real Phase 2 system

Generation on GPU is not bit-reproducible: bf16 sampling can pick a different token on near-ties, so the same seed can give a slightly different take.

Controlling code-switching

1. Explicit tags (most control). Wrap each span in <eng>, <pcm>, <ibo>, <yor> or <hau>. Keep punctuation inside the span it belongs to and put a space between spans. Untagged text is read as English.

<eng>this is dynamic accent,</eng> <pcm>e dey sound nice abi?</pcm>
<pcm>omo,</pcm> <eng>I'm very excited to announce this new model,</eng> <ibo>ọ na-eme m obi ụtọ,</ibo> <pcm>I no go lie give you</pcm>
<yor>Ẹ káàárọ̀ o, ṣé dáadáa ni?</yor> <eng>I will send you the report this afternoon.</eng>
<hau>Ina kwana, yaya aiki?</hau> <eng>The meeting has been moved to Friday morning.</eng>
<ibo>Ndewo nụ, ụmụnna m.</ibo> <pcm>Una well done o, I know say the road no easy.</pcm> <eng>We have three things to discuss this evening.</eng>
<eng>Please tell everyone that</eng> <yor>a ó pàdé ní ọ̀la.</yor>

A switch can fall mid-sentence (<eng>Please tell everyone that</eng> <yor>a ó pàdé ní ọ̀la.</yor>). The tag sets the pronunciation and accent of the words inside it; the voice stays the same.

2. --auto_tag for untagged text. tagger.py does word- and span-level language ID: Yoruba/Igbo/Hausa letters (ẹ ṣ / ị ụ ṅ / ɓ ɗ ƙ ƴ), small lexicons of frequent words, Pidgin markers ("dey", "abi", "omo", "wetin", "abeg", "una", "no go", "e don", "know say", pronoun + "go" + verb …) and clause context for ambiguous words ("na", "o", "ka", "ya"). It prints the tagged text so you can check and edit it:

python infer.py --auto_tag --tag_only --text "omo, I'm very excited to announce this new model, ọ na-eme m obi ụtọ, I no go lie give you"
# [tagged] <pcm>omo,</pcm> <eng>I'm very excited to announce this new model,</eng> <ibo>ọ na-eme m obi ụtọ,</ibo> <pcm>I no go lie give you</pcm>
python infer.py --auto_tag --text "..." --out out.wav            # tag, print, synthesise
python tagger.py "Kedu ka ị mere? Abeg no vex, I go call you for evening."   # tagger on its own

Write Yoruba and Igbo with their diacritics where you can; text without diacritics is much harder to identify. See the tagger accuracy below.

3. --force_lang reads everything in one language, e.g. --force_lang ibo.

4. --lang_map fixes words the tagger gets wrong, e.g. --lang_map "omo=pcm,sha=pcm,oga=pcm".

Gradio app (local)

python app.py --cosyvoice_dir ../CosyVoice      # http://127.0.0.1:7860  (--share for a public link)

It has a text box, an auto-tag toggle with an editable tagged-text preview, word overrides, a force-language option, version, voice and rate dropdowns, a seed, and a Compare button that plays Phase 3 and Flash side by side. The app launch has not been smoke-tested yet.

From Python

import types, soundfile as sf, infer
a = types.SimpleNamespace(model_dir="pretrained/CosyVoice2-0.5B", cosyvoice_dir="../CosyVoice", lm="", flow="",
                          prompts="prompts/prompts.json", voices="prompts/voices.json", dtype="auto", stitch=False,
                          rate="default", voice="waxal_ibo_5_female")
tts = infer.build("phase3", a)      # for "flash" also set frontend="codeswitch", frontend_lang="eng", no_diacritics=False,
                                     # experimental_translate=False, cv3_dir="pretrained/Fun-CosyVoice3-0.5B-2512"
wav = tts.say("<ibo>Kedu ka ị mere?</ibo> <eng>The meeting has been moved to Friday morning.</eng>", seed=0)
sf.write("out.wav", wav, tts.sr)

Samples

Flash (--version flash, Flash-DPO)

Seed 0, model pauses, speed 1.0, no post-processing. Inputs are written with diacritics unless noted.

sample	voice	text
igbo_pure_female	waxal_ibo_5_female	Chei, obi dị m ụtọ nke ukwuu ịkpọsa ụdị ọhụrụ a, ọ na-eme m obi ụtọ, agaghị m agha gị ụgha.
igbo_para_female	waxal_ibo_5_female	Ndewo nu, ụmụnna m. Taa bụ ụbọchị pụrụ iche n'obodo anyị. Anyị ga-ezukọ n'ama egwuregwu, rie nri, ma kparịta ụka maka ọdịnihu ụmụaka anyị.
yoruba_female / yoruba_male	waxal_yor_5_female / anchor_male	Ẹ káàsán o, ẹ̀yin ará. Inú mi dùn láti rí gbogbo yín lónìí. Ẹ jẹ́ ká jọ ṣiṣẹ́ pọ̀ fún ìlọsíwájú ìlú wa.
hausa_male	anchor_male	Barka da yamma, 'yan uwa. Muna farin cikin ganin ku a yau. Za mu fara taron nan ba da daɗewa ba, don haka ku zauna lafiya.
pidgin_female	waxal_ibo_5_female	Omo, this traffic no be here o. Abeg make una no vex, I go reach there small time. Wetin dey happen for your side?
codeswitch_ibo_pcm_eng	waxal_ibo_5_female	<ibo>Nne m, ọ dị mma, ka anyị gaa ahịa taa.</ibo> <pcm>Abeg make we no late o,</pcm> <eng>the shop closes at 6pm.</eng>
codeswitch_yor_eng	waxal_yor_5_female	<yor>Ẹ jọ̀ọ́, ẹ dúró díẹ̀,</yor> <eng>I will send you the report this afternoon.</eng> <yor>Ẹ ṣé púpọ̀.</yor>
codeswitch_dynacc	waxal_ibo_5_female	<eng>this is dynamic accent,</eng> <pcm>e dey sound nice abi?</pcm>
numbers_igbo	waxal_ibo_5_female	Ọnụ ahịa ya bụ ₦2,500, anyị ga-ahụ gị na 21/10/2026 n'elekere 4:30. (numbers verbalised by the front-end)
diacritics_yoruba_from_plain	waxal_yor_5_female	input without marks: "E kaaaro, se daadaa ni gbogbo yin wa? Oju ojo dara pupo loni." → front-end: Ẹ káàárọ̀, ṣé dáadáa ni gbogbo yín wà? Ojú ọjọ́ dára púpọ̀ lónì.

Phase 2 and Phase 3

Seed 0, rendered with infer.py. Inputs: para_ibo_pcm_eng, para_yor_eng, para_hau_eng_pcm (code-switched paragraphs), ibo_000 (Igbo), custom_dynacc ("this is dynamic accent, e dey sound nice abi?") and omo_announce ("omo, I'm very excited to announce this new model, ọ na-eme m obi ụtọ, I no go lie give you").

input	Phase 2, anchor_male	Phase 3, anchor_male	Phase 3, female
para_ibo_pcm_eng	mp3	mp3	waxal_ibo_5_female
para_yor_eng	mp3	mp3	waxal_yor_5_female
para_hau_eng_pcm	mp3	mp3	–
ibo_000	mp3	mp3	waxal_ibo_5_female
custom_dynacc	mp3	mp3	waxal_yor_5_female
omo_announce	mp3	mp3	waxal_ibo_5_female, waxal_yor_5_female

Older renders: samples/pcm_eng_dynamic_accent_seed{0,1,2}.mp3 (Phase 2, three seeds) and samples/prosody_v2/ (the rejected prosody-v2 post-processing, kept for reference).

Flash results

Same eval set and judges as the Phase 2 vs Phase 3 tables below (183 items plus code-switched and paragraph groups), seed 0, all groups pooled. Pace and pauses are ratios to native speakers reading the same texts (1.00 = native). Clipped endings = share of renders whose trailing silence is under 0.1 s (native recordings: 23.8%). SIM is ECAPA cosine to the voice's own real clips.

voice	system	CER	CER no tones	SIM	pace × native	pauses/s × native	clipped endings
anchor_male	Phase 3	0.174	0.146	0.815	0.92	1.11	9.1%
anchor_male	Flash	0.162	0.135	0.784	0.99	0.75	7.1%
waxal_ibo_5_female	Phase 3	0.164	0.132	0.683	1.00	0.74	22.8%
waxal_ibo_5_female	Flash	0.165	0.134	0.700	1.03	0.95	10.7%

• Flash lowers the anchor's CER (0.174 → 0.162) and clipped endings (9.1% → 7.1%), brings its pace to about native (0.92× → 0.99×), and more than halves clipped endings on the female Igbo voice (22.8% → 10.7%) at about native pace and pause rate.
• Per language with the anchor, Flash CER: Igbo 0.189 (Phase 3: 0.210), Yoruba 0.230 (0.243), Hausa 0.123 (0.146), English 0.026 (0.039), paragraphs 0.131 (0.168), code-switch 0.208 (0.212); Pidgin 0.227 (0.209) is worse, and the Pidgin judge is weak anyway.
• Known weakness: with anchor_male, Flash under-pauses in Igbo (0.40× native pauses per second) and Yoruba (0.30×). Long Igbo or Yoruba passages with the male voice can sound rushed; use a female voice or --compare with phase3 there.
• Speaker similarity of the anchor drops slightly (0.815 → 0.784), because Flash uses the stock CV3 flow zero-shot instead of the anchor-SFT flow.
• UTMOS caveat: UTMOS22 rates real native speech (3.53) below every synthetic system (3.8–4.3), so it rewards clean studio-like synthesis rather than nativeness. It was not used to choose the model (Phase 3 has the highest UTMOS22: 4.15 anchor / 4.27 female; Flash 3.89 / 4.16; Flash UTMOSv2 3.17 / 3.51). The choice rests on CER, pace and pauses relative to native speech, and clipped endings. Native-speaker MOS / blind A/B is still pending.
• Text front-end: diacritic restoration word accuracy Yoruba 0.862 (stripped input: 0.171), Igbo 0.968 (0.640).

Evaluation: Phase 2 vs Phase 3

183-item eval set (30 per language, 10 code-switched, 3 hand-written + 20 native long-form paragraphs), anchor voice, seed 0. CER is ASR round-trip: MMS-1b-all for Igbo/Hausa/Yoruba, Whisper-large-v3-turbo for English and Pidgin, the lower of the two for code-switched items and paragraphs. "No tones" (in brackets) strips diacritics. SIM is ECAPA cosine to the anchor (ceiling about 0.84 on real held-out anchor clips). The Pidgin judge is weak: real Pidgin recordings score about 0.43.

system	ibo	hau	yor	pcm	eng	CS	para (hand / native)	SIM
phase2 as shipped (sentence stitching)	0.215 (0.171)	0.194	0.234 (0.149)	0.196	0.035	0.163	0.192 / 0.154	0.785
Phase 2 LM, Phase 3 decoding	0.243 (0.189)	0.169	0.275 (0.175)	0.195	0.046	0.185	0.195 / 0.158	0.787
phase3	0.219 (0.173)	0.151	0.233 (0.145)	0.202	0.043	0.151	0.170 / 0.155	0.788

Same decoding, Phase 3 LM vs Phase 2 LM: lower CER in every language except Pidgin, same speaker similarity.

system	comma pause s	sentence pause s	pauses / s	pause-length W1 vs anchor	trailing silence s	clipped endings (< 0.1 s)	articulation syl/s
real anchor recordings	0.417	0.543	0.624	0	0.16	15%	4.43
Phase 2 LM, Phase 3 decoding	0.403	0.389	0.329	0.194	0.08	63%	4.36
phase3	0.422	0.453	0.319	0.161	0.17	11%	4.44

Female voices (29-item subset, phase3):

voice	CER	no-tone CER	SIM to the speaker's own real clips	clipped endings
anchor_male (same items)	0.182	0.141	0.12–0.13 (it is a different speaker)	17%
waxal_ibo_5_female	0.149	0.101	0.64	21%
waxal_yor_5_female	0.171	0.134	0.76	62%

Naturalness has not been rated by native speakers (no MOS or blind A/B yet). The numbers above are automatic proxies.

Auto-tagger accuracy

Word-level accuracy of tagger.py against hand-tagged text (no GlotLID / with --lid glotlid):

test set	words	rule-based	+ GlotLID	exact sentences (rule-based)
the 6 sample inputs above	145	1.000	1.000	6/6
10 new mixed sentences, written before the lexicons	113	0.991	0.991	9/10
12 code-switched eval items (overlaps the samples)	206	0.995	1.000	11/12
150 single-language eval sentences (real WAXAL/FLEURS text, never looked at while building the lexicons)	1,697	0.898	0.915	115/150

On the held-out single-language set, per language (rule-based / + GlotLID): English 0.99 / 1.00, Igbo 1.00 / 1.00, Yoruba 1.00 / 1.00, Hausa 0.89 / 0.94, Pidgin 0.63 / 0.65. Pidgin written without markers looks like English and gets tagged eng. The mixed test sentences were written and gold-tagged by the tagger's author, not native speakers, so their scores are optimistic. Check the printed tags, and use explicit tags or --lang_map when it matters.

Limitations

• Flash (flash): the male anchor under-pauses in Igbo and Yoruba (0.40× / 0.30× native pause rate); Pidgin CER is slightly worse than Phase 3; CUDA only; the translate-first mode is experimental and off by default (MADLAD-400 loops on long inputs and is weak on Pidgin); diacritic restoration can still pick the wrong tone on ambiguous words, so write marks yourself where you can. The limitations below for Phase 3 (no laughter, Pidgin data, accent after a switch) apply to Flash too; the pace gap is largely closed for Flash.
• Pace (phase2/phase3): speech is still 13–28% slower than native speakers (Igbo 4.60 vs 5.30 syl/s, Hausa 3.77 vs 5.26 syl/s), and there are about half as many pauses per second as in real speech. It can sound careful rather than fully native.
• Pidgin is the weakest language: only about 5 h of training data, no reliable ASR judge, and the auto-tagger's Pidgin/English split is its weakest point (~0.63 word accuracy on plain Pidgin).
• No laughter or other expressive events: the training data has effectively no laughter, so tags like [laughter] do nothing useful. [pause]/[breath] were learned from data, but there is no reliable user control over them yet.
• English after a Nigerian-language span keeps a strong local accent.
• Yoruba tone quality needs native listening; the automatic tone metric is saturated.
• Female voices are zero-shot; waxal_yor_5_female copies its short prompt (short pauses, clipped endings).
• Not evaluated for numbers, abbreviations or URLs; write numbers as words.
• Do not use it to impersonate real people. The voices are WAXAL Media Trust speakers, released under CC-BY-SA-4.0 for speech technology.

What is in the repo

path	what it is	licence
checkpoints/flash/flash_dpo/	Flash LM (best.pt, sha256 f59a6fc0…7e3a307, CosyVoice3 LM + language/speaker/rate embeddings), config.json, spk2id.json, DPO args.json, log.jsonl	CC-BY-SA-4.0
checkpoints/flash/diacritics_byt5/	Yoruba/Igbo diacritic-restoration model (fine-tune of google/byt5-small, Apache-2.0) used by the Flash front-end	CC-BY-SA-4.0
checkpoints/phase3/p3_lm_full/	Phase 3 LM (best.pt, val step 4,500), config.json (rate, sampler, one-pass, points at the shared Phase 2 flow), spk2id.json, args.json, log.jsonl	CC-BY-SA-4.0
checkpoints/phase2/p2_lm_B/	Phase 2 LM (best.pt) and spk2id.json	CC-BY-SA-4.0
checkpoints/phase2/p2_flow_sft/best.pt	anchor-SFT flow-matching decoder, used by both versions	CC-BY-SA-4.0
prompts/	anchor prompt clips (clip0..3, prompts.json with transcripts and S3 tokens) and female voice prompts (voice_*.wav, voices.json), all from WAXAL	CC-BY-SA-4.0
samples/	example renders (samples/flash/ for Flash)	CC-BY-SA-4.0
settings.json	versions, voices, recommended pairing	Apache-2.0
infer.py, tagger.py, app.py	inference CLI, auto-tagger, Gradio app	Apache-2.0
flash_lm.py, frontend/	Flash model and synthesis code (training code for reference; the data manifest is not shipped), Flash text front-end (normalisation, diacritics, experimental MT)	Apache-2.0
prompts/flash_voices_text.json	transcripts of the voice clips, used as Flash LM prompts	CC-BY-SA-4.0
code/	the original training and synthesis code, for reference	Apache-2.0
requirements.txt, cosyvoice_requirements.txt	dependencies (cosyvoice_requirements.txt is CosyVoice's own file at the commit used)	–
assets/	banner	–

Training data and attribution

The model weights are trained on, and the voice prompts are taken from, the datasets below. Several are CC-BY-SA-4.0, so the weights, voice prompts and samples are released under CC-BY-SA-4.0 (share-alike).

dataset	used for	licence	attribution
WAXAL, Media Trust TTS subsets (Igbo, Yoruba, Hausa, Nigerian Pidgin)	main training data; the anchor voice (Yoruba speaker 4) and the female prompt voices (Igbo speaker 5, Yoruba speaker 5)	CC-BY-SA-4.0 (as listed on the dataset card for the Media Trust subsets)	Google and Media Trust, WAXAL (arXiv:2602.02734)
Open.Bible Igbo (Biblica® Open Igbo Contemporary Bible™, Audio Edition)	Igbo (Phase 3)	CC-BY-SA-4.0	Biblica, Inc. via open.bible; Biblica® and Open Igbo Contemporary Bible™ are trademarks of Biblica, Inc.
NaijaS2ST (Igbo)	Igbo, many native speakers (Phase 3)	CC-BY-4.0	McGill-NLP, NaijaS2ST (arXiv:2604.16287)
IgboAPI (ibo-dict, ibo-dict-expansion)	Igbo sentences (Phase 3)	CC-BY-4.0	Nkọwa okwu / IgboAPI contributors
IgboSynCorp	natural Igbo speech (Phase 3); Flash adds pseudo-transcripts of its untranscribed audio	CC0-1.0	University of Ibadan and Afe Babalola University (Lacuna Fund)
FLEURS (English, Hausa, Igbo, Yoruba)	multi-speaker data	CC-BY-4.0	Google, FLEURS (Conneau et al., 2022)
BibleTTS (Yoruba, Hausa)	Yoruba and Hausa	CC-BY-SA-4.0	Meyer et al., BibleTTS (2022); Biblica / open.bible audio
OpenSLR SLR70 (Nigerian English)	English	CC-BY-SA-4.0	Google, crowdsourced Nigerian English
OpenSLR SLR86 (Yoruba)	Yoruba	CC-BY-SA-4.0	Google, crowdsourced Yoruba
code-switched pairs	about 9 h of synthetic code-switch training pairs	follows the source clips (CC-BY-SA-4.0 / CC-BY-4.0)	built by concatenating clips from the sets above, following the Google 2019 code-switching recipe (Zhang et al., 2019); no separate Google dataset
base model FunAudioLLM/Fun-CosyVoice3-0.5B-2512	Flash: LM initialisation, flow, vocoder, tokenizers	Apache-2.0	FunAudioLLM / Alibaba Tongyi Lab, CosyVoice 3
google/byt5-small	base of the Flash diacritic-restoration model (fine-tuned on transcripts of the datasets above)	Apache-2.0	Google, ByT5 (Xue et al., 2022)
base model FunAudioLLM/CosyVoice2-0.5B	LM initialisation, flow, vocoder, tokenizers	Apache-2.0	FunAudioLLM / Alibaba Tongyi Lab, CosyVoice 2 (Du et al., 2024)
optional GlotLID (not bundled)	--lid glotlid in the tagger	Apache-2.0 plus notices	Kargaran et al., GlotLID (EMNLP 2023)

Flash's training data is the licensed set above (the same sources as Phase 3, re-weighted with less Bible narration and slow read speech, plus IgboSynCorp pseudo-transcripts); its DPO pairs are the model's own renders of eval-style prompts, scored automatically. Excluded on licence grounds from every released checkpoint, Flash included: African Voices / WaZoBiaSpeech (its cards ban TTS use), NaijaVoices (non-commercial), and non-commercial TTS weights. The optional MADLAD-400-3B-MT translator (Apache-2.0) is not bundled; NLLB was avoided because it is non-commercial.

Licence

• Model weights, voice prompts and samples (checkpoints/, prompts/, samples/): CC-BY-SA-4.0. You may use them commercially. You must credit Modular and the datasets listed above, and anything you derive from the weights (fine-tunes, merges) must be shared under CC-BY-SA-4.0 too.
• Code (infer.py, tagger.py, app.py, flash_lm.py, frontend/, code/, settings.json): Apache-2.0.
• The base models, CosyVoice2-0.5B and Fun-CosyVoice3-0.5B-2512 (and byt5-small for the diacritic model), are Apache-2.0; their notices still apply to the parts they contribute.

Citation

@misc{dynamicaccent2026,
  title  = {Dynamic Accent: Code-switching TTS for Nigerian languages},
  author = {Modular},
  year   = {2026},
  url    = {https://huggingface.co/Modularcomputing/dynamic-accent-preview}
}