File size: 2,732 Bytes
7bf2e0e 89af8f2 bcfe9f7 7bf2e0e 9a9580c 7bf2e0e bcfe9f7 7bf2e0e 9a9580c 7bf2e0e 9a9580c 7bf2e0e 9a9580c 7bf2e0e 5943fbd c67eccb 5943fbd eec2a9d 7bf2e0e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
---
language:
- tel
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- jayasuryajsk/google-fleurs-te-romanized
model-index:
- name: Wishper-Large-V3-spoken_telugu_romanized
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Wishper Large V3 - Romanized Spoken Telugu
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Telugu Romanized 1.0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.5009
- eval_wer: 68.1275
- eval_runtime: 591.6137
- eval_samples_per_second: 0.798
- eval_steps_per_second: 0.1
- epoch: 8.6207
- step: 1000
## Model description
The model is trained to transcipt Telugu conversations in Romanized script, that most people uses in day to day life.
## Intended uses & limitations
Limitations: Sometimes, it translates the audio to english directly. Working on this to fix it.
## Training and evaluation data
Gpt 4 api was used to convert ``` google-fleurs ``` telugu labels to romanized script. I used english tokenizer, since the script is in english alphabet to train the model.
## Usage
```python
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "jayasuryajsk/whisper-large-v3-Telugu-Romanized"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
max_new_tokens=128,
chunk_length_s=30,
batch_size=16,
return_timestamps=True,
torch_dtype=torch_dtype,
device=device,
)
result = pipe("recording.mp3", generate_kwargs={"language": "english"})
print(result["text"])
```
Try this on https://colab.research.google.com/drive/1KxWSaxZThv8PE4mDoLfJv0O7L-5hQ1lE?usp=sharing
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 20
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|