Model reference · open weights

marathi_lrec2020

Audio espnet Speech→text 1 build Open weights 2 dl/mo

marathi_lrec2020 is an open-weight audio or speech model from espnet. marathi_lrec2020 (BF16) weighs 33 MB; the smallest configuration that runs it is RTX 3060 12 GB.

  • marathi_lrec2020 is an automatic speech recognition model developed by espnet for the Marathi language.
  • It utilizes a character-level Conformer encoder and Transformer decoder trained on the IndicCorpora Marathi subset.
  • The model is distributed under the cc-by-4.0 license.

Summary of the espnet/marathi_lrec2020 model card, 2026-10-01

What it is

Released byespnet
Released2026-04-19
VRAM33 MB for the weights

What it runs on

Memory and cards for marathi_lrec2020 (BF16)

33 MBweights, file size
1.6 GBruntime overhead
CardOne streamMemory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What espnet says about marathi_lrec2020

Read the model card

ESPnet2 ASR model

espnet/marathi_lrec2020

This model was trained by Aniket Tathe using marathi_lrec2020 recipe in espnet.

Demo: How to use in ESPnet2

Follow the ESPnet installation instructions if you haven't done that already.

cd espnet
git checkout 6241a3e3ad9fef6a686ac82c6a7799d40d96cd27
pip install -e .
cd egs2/marathi_lrec2020/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/marathi_lrec2020

RESULTS

Environment

  • Date: Sat Oct 25 08:19:46 UTC 2025
  • Python version: 3.9.23
  • ESPnet version: 202509
  • PyTorch version: 2.3.0+cu121
  • CUDA version: 12.1
  • ESPnet Git hash (upstream): 53e09761cb164b28f299e178262bf2056d8059d7
    • Commit date: Fri Oct 24 11:26:46 2025 +0900

Marathi ASR — marathi_lrec2020

Recipe for Marathi ASR on the IndicCorpora Marathi subset.

Training uses conf/train_asr_transformer.yaml (character Conformer: 3 blocks, 256-dim encoder, batch_bins: 16000000, accum_grad: 4, Adam lr: 0.0005, warmup 20k, SpecAugment, hybrid CTC/attention ctc_weight: 0.3).

Decoding without LM: conf/decode_asr.yaml (lm_weight: 0.0). Decoding with LM (match reported fusion): conf/decode_asr_lm.yaml (beam 20, ctc_weight: 0.5, lm_weight: 0.3).


Test-set decoding (marathi_test)

Beam 20, CTC weight 0.5 unless noted.

Beam 20, CTC 0.5 (no LM)
CorrSubDelInsErrS.Err
CER88.97.14.01.913.077.7
WER73.823.82.43.229.478.5
Beam 20, CTC 0.5, LM weight 0.3
CorrSubDelInsErrS.Err
CER89.06.64.41.712.674.3
WER76.021.62.43.027.075.0

Dataset reference

P. Jyothi et al., “IndicCorpora: A Large Multilingual Corpus for Indic Languages.” IIT Bombay IndicCorpora — Marathi

ASR config

config: conf/tuning/train_asr_transformer.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: exp/asr_marathi_conf_old_try_3conf_16Mbin_4grad
ngpu: 1
seed: 777
num_workers: 8
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
use_deepspeed: false
deepspeed_config: null
gradient_as_bucket_view: true
ddp_comm_hook: null
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
use_tf32: false
collect_stats: false
write_collected_feats: false
max_epoch: 60
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - loss
    - min
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
no_forward_run: false
resume: false
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
use_adapter: false
adapter: lora
save_strategy: all
adapter_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 16000000
valid_batch_bins: null
category_sample_size: 10
upsampling_factor: 0.5
category_upsampling_factor: 0.5
dataset_upsampling_factor: 0.5
dataset_scaling_factor: 1.2
max_batch_size: null
min_batch_size: 1
train_shape_file:
- exp/asr_stats_raw_char/train/speech_shape
- exp/asr_stats_raw_char/train/text_shape.char
valid_shape_file:
- exp/asr_stats_raw_char/valid/speech_shape
- exp/asr_stats_raw_char/valid/text_shape.char
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
chunk_default_fs: null
chunk_max_abs_length: null
chunk_discard_short_samples: true
train_data_path_and_name_and_type:
-   - dump/raw/marathi_train_sp/wav.scp
    - speech
    - sound
-   - dump/raw/marathi_train_sp/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - dump/raw/marathi_dev/wav.scp
    - speech
    - sound
-   - dump/raw/marathi_dev/text
    - text
    - text
multi_task_dataset: false
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
allow_multi_rates: false
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
    lr: 0.0005
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 20000
token_list:
-
-
-
- ा
- े
- र
- ्
- क
- स
- ल
- म
- ि
- ं
- ी
- य
- त
- न
- व
- ग
- ह
- ट
- च
- प
- ड
- आ
- .
- ज
- श
- ो
- द
- ब
- अ
- ू
- ु
- '?'
- ण
- इ
- ध
- ए
- फ
- ख
- ॉ
- ॅ
- ळ
- ँ
- भ
- थ
- ठ
- ई
- झ
- ष
- उ
- ऑ
- ऊ
- घ
- ढ
- ै
- ओ
- '2'
- (
- )
- '0'
- ृ
- ौ
- '-'
- '3'
- '1'
- ऐ
- ‍
- '5'
- '8'
- छ
- '4'
- '"'
- ','
- '9'
- औ
- '!'
- ़
- ञ
- '7'
- '6'
- ९
- ऋ
- e
- g
- ३
- X
- १
- ०
-
init: xavier_uniform
input_size: null
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: null
    zero_infinity: true
    brctc_risk_strategy: exp
    brctc_group_strategy: end
    brctc_risk_factor: 0.0
joint_net_conf: null
use_preprocessor: true
use_lang_prompt: false
use_nlp_prompt: false
token_

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

How it works

How audio & music work

Audio or textinputAudio modelrecognise / synthesiseText or audiooutputSpeech-to-text turns audio into text; text-to-speech and music models turn text into audio.
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