F5-TTS — Quebec French (Québécois) finetune
A zero-shot voice-cloning TTS checkpoint specialized for Quebec French. Give it a
short reference clip of any voice and it clones that voice while applying Québécois
pronunciation, prosody, and vocabulary.
Finetune of
F5-TTS (
F5TTS_Base architecture,
vocos vocoder), warm-started from
RASPIAUDIO/F5-French-MixedSpeakers-reduced
and trained on a Québécois-accent-tagged subset of Mozilla Common Voice (fr).
License note: the warm-start base checkpoint is CC-BY-NC-4.0. This finetune
inherits that restriction — non-commercial use only. The training data itself is
CC0-1.0.
Quickstart
1pip install f5-tts
2
3f5-tts_infer-cli \
4 --model F5TTS_Base \
5 --ckpt_file model_quebec_v1.safetensors \
6 --vocab_file vocab.txt \
7 --ref_audio your_reference.wav \
8 --ref_text "Transcript of the reference clip." \
9 --gen_text "Je m'en vais au dépanneur chercher de la liqueur pis des chips."
Download the two required files with:
1huggingface-cli download tontate/f5-tts-quebec-french-finetune \
2 model_quebec_v1.safetensors vocab.txt
Tips: use a clean 3–10 s reference clip; leave --ref_text "" to auto-transcribe it.
The checkpoint clones any voice — a Québécois-accented reference gives the most
consistent results.
Audio samples
Generated with the final checkpoint (voice
qc_a); more in
samples/.
« Il faut que je stationne mon char devant le dépanneur. »
« Fait qu'on a fini par jaser toute la soirée sur la galerie. »
« Ça coûte quatre-vingt-dix-sept dollars et trente-cinq sous. »
Files
| File | Size | Use |
|---|
model_quebec_v1.safetensors | 1.35 GB | Recommended for inference — EMA weights only |
model_last.pt | 5.39 GB | Full training checkpoint (model + EMA + optimizer) — for resuming training |
checkpoints/model_9000.pt, checkpoints/model_12000.pt | 5.39 GB each | Intermediate training checkpoints |
vocab.txt | 14 kB | Tokenizer vocab (unchanged from base / F5-TTS default) |
scripts/prep_common_voice_qc.py | — | Dataset preparation script (reproducible) |
dataset/processed_qc_dataset.tar.gz | — | The processed training set |
samples/ | — | Eval-sentence audio (2 voices) + per-checkpoint training-trajectory samples |
model_quebec_v1.safetensors contains exactly the EMA weights of model_last.pt
(bitwise-identical extraction, verified), repackaged for safe, fast loading.
Training
- Data: Common Voice fr (Scripted Speech 26.0), CC0-1.0, filtered to clips with
Québécois/Canadien
accent metadata. Final set: 11,542 clips, 281 speakers,
12.01 hours (after resample to 24 kHz mono, silence trim, dedup, 1–15 s length
filter). Prep script included in scripts/.
- Run: 50 epochs / 67,750 updates with the F5-TTS finetuning trainer on a single
RTX A5000 (24 GB), warm-started from the base checkpoint with its vocab unchanged.
- Training loss plateaued early (≈ epoch 2); trajectory samples for every 1,000
updates are in
samples/training_trajectory/ if you want to compare checkpoints.
Evaluation
Evaluated on a 50-sentence Québécois test battery targeting dialect-specific
phenomena (affrication, vowel laxing, /a/–/ɑ/ contrast & diphthongs, QC
lexicon/elisions, numbers, question particles & prosody), synthesized with
3 reference voices, and compared against the warm-start base model
(
RASPIAUDIO/F5-French-MixedSpeakers-reduced, France-accented). Scripts and
the sentence list are in
scripts/eval/.
| Metric | Base (France) | This model (Québec) |
|---|
| WER ↓ (faster-whisper large-v3) | 0.067 | 0.077 |
| CER ↓ | 0.041 | 0.043 |
| Speaker similarity ↑ (ECAPA cosine) | 0.479 | 0.675 |
| Accent probe: mean P(Québec) ↑ | 0.17 | 0.40 |
- Intelligibility is preserved: WER/CER are comparable; the finetune's small
gap concentrates in QC lexicon and elisions ("t'sé", "pis") that the ASR
normalizes to standard French spellings.
- Voice cloning improves markedly on Québécois reference voices (+0.20
cosine), as expected from in-domain training.
- Accent probe: a logistic-regression classifier over mid-layer XLS-R-300m
embeddings, trained to separate real Québec-tagged vs. European-tagged
(France/Belgium/Switzerland) Common Voice fr clips — held-out accuracy 0.82 on
speaker-disjoint test speakers. Applied to the generated audio, it rates the
probability the audio sounds Québécois.
- Human check: informal native-listener review judged the output distinctly
Québécois (satisfactory) rather than European French.
Caveats: reference clips are drawn from the training corpus (speaker-similarity
numbers are in-domain); the accent probe is a proxy, not a listening test;
number-heavy sentences are hardest for both models — write numbers out in words.
Limitations
- Non-commercial license (inherited from the base checkpoint).
- 12 hours of training data: rare words, heavy anglicisms, and long-form prosody may
be less stable; write numbers out in words for best results.
- Common Voice accent tags are self-reported; the training accent mix is
broadly Québécois/Canadian rather than a single regional variety.
