Persian-IPA-to-Speech-F5 is a Persian speech synthesis checkpoint fine-tuned
from F5-TTS v1 Base. It is conditioned directly on raw Unicode IPA sequences
rather than ordinary Persian orthography.
This model does not accept regular Persian text directly. Convert the
input to the model's supported IPA representation before synthesis.
Highlights
Direct Persian IPA-to-speech synthesis
Custom Unicode codepoint vocabulary
24 kHz mono waveform output
Reference-audio voice conditioning
Inference-ready EMA weights in Safetensors format
Reproducible checkpoint and vocabulary checksums
Files
File
Purpose
model.safetensors
Final inference-ready EMA checkpoint
vocab.txt
Custom tokenizer vocabulary; 2,547 unique symbols
config.yaml
Model, tokenizer, audio, and compatibility configuration
manifest.json
Artifact provenance and validation results
SHA256SUMS
Integrity checksums
examples/
Generated audio samples and their IPA metadata
The model contains 364 tensors. Its text embedding shape is [2548, 512],
which corresponds to 2,547 vocabulary symbols plus F5-TTS's internal filler
row.
Installation
pip install persian-ipa-to-speech-f5
FFmpeg must be available on the system. For GPU inference, install the PyTorch
build matching your CUDA version first.
The reference transcription must match the spoken reference audio. Use NFC
Unicode normalization and preserve the same IPA conventions used by the model.
End-to-end Persian text to speech
For ordinary Persian input, use
KiaBush/persian-text-to-ipa-byt5
as the grapheme-to-phoneme front end, then pass its IPA output to this model.
Review the generated IPA for names, numbers, abbreviations, dialectal forms,
and other context-dependent pronunciations before production synthesis. For
long input, follow the Text-to-IPA model's chunking guidance.
License
The model checkpoint is a derivative of F5-TTS v1 Base and is licensed under
CC-BY-NC-4.0. The
non-commercial restriction applies to the model. F5-TTS source code is
separately available under the MIT License. Rights or consent associated with
training data, reference voices, and generated audio are not granted by the
model license and remain the user's responsibility.
Audio samples
The following files are generated outputs. Reference recordings are not
redistributed with these previews.
Compatible F5-TTS base: v1.1.22 with the linked raw-input patch
Limitations
Input must already be valid IPA; this repository does not include a Persian
grapheme-to-phoneme converter.
The model may mispronounce IPA conventions or symbols underrepresented during
fine-tuning.
Output quality depends strongly on reference-audio quality and transcription
accuracy.
Voice conditioning does not imply identity ownership or permission to imitate
a speaker.
Performance may vary across accents, speaking styles, and long utterances.
Responsible use
Only use reference voices with the speaker's authorization. Do not use this
model for impersonation, fraud, deceptive media, harassment, or bypassing voice
authentication. Clearly disclose synthetic audio where listeners could mistake
it for a real recording.
Code and attribution
The compatible inference implementation is maintained at
KiaBush/F5-TTS
and is derived from the MIT-licensed
SWivid/F5-TTS project.
The license for this fine-tuned weight release should be reviewed independently
from the upstream code license and the rights governing training and example
audio.
Authors and contributors
Ali Akbar Kiaei — research,
model training, engineering, and release
Mahnaz Bush — data preparation,
model training, evaluation, research, and documentation
