Bangla Parler-TTS (fine-tuned from Indic Parler-TTS)
Bangla (Bengali) text-to-speech model, fine-tuned from
ai4bharat/indic-parler-tts on a curated,
quality-filtered subset of the SUBAK.KO Bangla speech corpus. Like the base
model, it generates speech conditioned on a text prompt (what to say, in
Bangla) and a natural-language description (how to say it: gender, pace,
clarity).
Training data
The training set was built by curating SUBAK.KO (an ASR corpus) into a TTS-suitable dataset:
- per-clip quality scoring (SNR proxy, clipping, duration 1-15 s)
- kept only the cleanest clips (SNR floor + per-speaker balancing), ~10.5k train clips / 800 validation / 800 test
- style descriptions rebuilt to be name-free and attribute-based, with speaker gender taken from corpus speaker codes (ground truth), e.g. "A Bengali female speaker speaks at a moderate pace, in a very clear sound."
Training setup
- Base:
ai4bharat/indic-parler-tts(text encoder frozen; decoder + LM head trained) - 8 epochs, ~2.6k optimizer steps, effective batch 32 (4 x 8 grad-accum)
- LR 1.5e-5, cosine schedule, 200 warmup steps, weight decay 0.01
- bfloat16, gradient checkpointing, SDPA attention, single A100-40GB
Installation
Using Parler-TTS is as simple as "Bengali". Simply install the library once:
pip install git+https://github.com/huggingface/parler-tts.git
Usage
import torch
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer
import soundfile as sf
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = ParlerTTSForConditionalGeneration.from_pretrained("Tanmoyshome/bn-parler-tts").to(device)
tokenizer = AutoTokenizer.from_pretrained("Tanmoyshome/bn-parler-tts")
description_tokenizer = AutoTokenizer.from_pretrained(model.config.text_encoder._name_or_path)
prompt = "রোগ যদি ধরতে চাও রোগীর ইতিহাস জেনে নাও তাতে কিছু তথ্য নাও সেই মত পথ্য দাও"
description = ("A Bengali female speaker speaks at a moderate pace "
"in a very clear environment.")
description_input_ids = description_tokenizer(description, return_tensors="pt").to(device)
prompt_input_ids = tokenizer(prompt, return_tensors="pt").to(device)
generation = model.generate(
input_ids=description_input_ids.input_ids,
attention_mask=description_input_ids.attention_mask,
prompt_input_ids=prompt_input_ids.input_ids,
prompt_attention_mask=prompt_input_ids.attention_mask,
)
audio_arr = generation.cpu().numpy().squeeze()
sf.write("bn_tts_out.wav", audio_arr, model.config.sampling_rate)
Requires pip install git+https://github.com/huggingface/parler-tts.git soundfile.
Prompting tips
Descriptions in training follow a fixed attribute style; you will get the most reliable control by mirroring it:
- gender: "A Bengali male speaker" / "A Bengali female speaker"
- pace: "at a slow / moderate / fast pace", "speaks quickly / slowly"
- clarity: "in a very clear recording", "in a clear environment"
Limitations
- Trained on multi-speaker read speech; voice identity may vary between generations (no named-voice control).
- Descriptions control gender / pace / clarity; fine-grained emotion or named speakers are not supported.
- Bangla only; other Indic languages should use the base model.
Base model / attribution
Fine-tuned from ai4bharat/indic-parler-tts
(AI4Bharat), built on the Parler-TTS architecture. Please also refer to the
base model card for architecture details and broader usage guidance.