# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("pepoo20/content")
model = AutoModelForCTC.from_pretrained("pepoo20/content")Quick Links
Umong/wav2vec2-large-mms-1b-bengali
This model is a fine-tuned version of Umong/wav2vec2-large-mms-1b-bengali on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.8979
- eval_runtime: 77.1222
- eval_samples_per_second: 6.483
- eval_steps_per_second: 1.621
- epoch: 0.84
- step: 9000
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: 2e-06
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 2000
- num_epochs: 1
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
- Downloads last month
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Model tree for pepoo20/content
Base model
facebook/mms-1b-all Finetuned
Umong/wav2vec2-large-mms-1b-bengali
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="pepoo20/content")