Instructions to use vrclc/W2V2-BERT-withLM-Malayalam-Studio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vrclc/W2V2-BERT-withLM-Malayalam-Studio with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="vrclc/W2V2-BERT-withLM-Malayalam-Studio")

# Load model directly
from transformers import AutoProcessor, AutoModelForCTC

processor = AutoProcessor.from_pretrained("vrclc/W2V2-BERT-withLM-Malayalam-Studio")
model = AutoModelForCTC.from_pretrained("vrclc/W2V2-BERT-withLM-Malayalam-Studio")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vrclc/W2V2-BERT-withLM-Malayalam-Studio with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vrclc/W2V2-BERT-withLM-Malayalam-Studio"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vrclc/W2V2-BERT-withLM-Malayalam-Studio",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vrclc/W2V2-BERT-withLM-Malayalam-Studio

SGLang

How to use vrclc/W2V2-BERT-withLM-Malayalam-Studio with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "vrclc/W2V2-BERT-withLM-Malayalam-Studio" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vrclc/W2V2-BERT-withLM-Malayalam-Studio",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "vrclc/W2V2-BERT-withLM-Malayalam-Studio" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vrclc/W2V2-BERT-withLM-Malayalam-Studio",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vrclc/W2V2-BERT-withLM-Malayalam-Studio with Docker Model Runner:
```
docker model run hf.co/vrclc/W2V2-BERT-withLM-Malayalam-Studio
```

W2V2-BERT-withLM-Studio-Malayalam

This model is a fine-tuned version of facebook/w2v-bert-2.0 on IMASC, OpenSLR Malayalam Train split, Festvox Malayalamdataset. It achieves the following results on the evaluation set:

Loss: 0.1587
Wer: 0.1157

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.0335	0.4932	600	0.3654	0.4387
0.1531	0.9864	1200	0.2373	0.3332
0.1074	1.4797	1800	0.2069	0.2953
0.0928	1.9729	2400	0.2146	0.2814
0.0734	2.4661	3000	0.1947	0.2433
0.0678	2.9593	3600	0.1938	0.2406
0.0522	3.4525	4200	0.1566	0.2053
0.0493	3.9457	4800	0.1649	0.1988
0.0366	4.4390	5400	0.1417	0.1834
0.0372	4.9322	6000	0.1542	0.1749
0.028	5.4254	6600	0.1476	0.1620
0.0263	5.9186	7200	0.1388	0.1622
0.0195	6.4118	7800	0.1384	0.1495
0.0185	6.9051	8400	0.1351	0.1383
0.0136	7.3983	9000	0.1404	0.1344
0.0119	7.8915	9600	0.1253	0.1276
0.0087	8.3847	10200	0.1443	0.1284
0.0066	8.8779	10800	0.1475	0.1252
0.0049	9.3711	11400	0.1577	0.1227
0.0038	9.8644	12000	0.1587	0.1157

Framework versions

Transformers 4.42.2
Pytorch 2.1.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

Downloads last month: 11

Safetensors

Model size

0.6B params

Tensor type

F32

Model tree for vrclc/W2V2-BERT-withLM-Malayalam-Studio

Base model

facebook/w2v-bert-2.0

Finetuned

(473)

this model

Datasets used to train vrclc/W2V2-BERT-withLM-Malayalam-Studio

Spaces using vrclc/W2V2-BERT-withLM-Malayalam-Studio 2

Collection including vrclc/W2V2-BERT-withLM-Malayalam-Studio

VRCLC Malayalam ASR : Models

Collection

6 items • Updated Aug 6, 2024

Evaluation results

WER on OpenSLR Malayalam -Test
test set self-reported

8.820
WER on Goole Fleurs
test set self-reported

32.010
WER on Common Voice 16 Malayalam
test set self-reported

52.720