Versions:
- CUDA: 12.1
- cuDNN Version: 8.9.2.26_1.0-1_amd64
- tensorflow Version: 2.12.0
- torch Version: 2.1.0.dev20230606+cu12135
- transformers Version: 4.30.2
- accelerate Version: 0.20.3
Model Benchmarks:
RAM: 3 GB (Original_Model: 6GB)
VRAM: 3.7 GB (Original_Model: 11GB)
test.wav: 23 s (Multilingual Speech i.e. English+Hindi)
- Time in seconds for Processing by each device
Device Name float32 (Original) float16 CudaCores TensorCores 3060 2.2 1.3 3,584 112 1660 Super OOM 6 1,408 N/A Collab (Tesla T4) - - 2,560 320 Collab (CPU) - N/A N/A N/A M1 (CPU) - - N/A N/A M1 (GPU -> 'mps') - - N/A N/A - NOTE: TensorCores are efficient in mixed-precision calculations
- CPU -> torch.float16 not supported on CPU (AMD Ryzen 5 3600 or Collab CPU)
Punchuation: Sometimes False ('I don't know the exact reason why this is happening')
Model Error Benchmarks:
- WER: Word Error Rate
- MER: Match Error Rate
- WIL: Word Information Lost
- WIP: Word Information Preserved
- CER: Character Error Rate
Hindi to Hindi (test.tsv) Common Voice 14.0
Test done on RTX 3060 on 1000 Samples
WER | MER | WIL | WIP | CER | |
---|---|---|---|---|---|
Original_Model (30 min) | 43.99 | 41.65 | 59.47 | 40.52 | 16.23 |
This_Model (20 min) | 44.64 | 41.69 | 59.53 | 40.46 | 16.80 |
Hindi to English (test.csv) Custom Dataset
Test done on RTX 3060 on 1000 Samples
WER | MER | WIL | WIP | CER | |
---|---|---|---|---|---|
Original_Model (30 min) | - | - | - | - | - |
This_Model (20 min) | - | - | - | - | - |
English (LibriSpeech -> test-clean)
Test done on RTX 3060 on ___ Samples
WER | MER | WIL | WIP | CER | |
---|---|---|---|---|---|
Original_Model | - | - | - | - | - |
This_Model | - | - | - | - | - |
English (LibriSpeech -> test-other)
Test done on RTX 3060 on ___ Samples
WER | MER | WIL | WIP | CER | |
---|---|---|---|---|---|
Original_Model | - | - | - | - | - |
This_Model | - | - | - | - | - |
- 'jiwer' library is used for calculations
Code for conversion:
Usage
A file __init__.py
is contained inside this repo which contains all the code to use this model.
Firstly, clone this repo and place all the files inside a folder.
Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/devasheeshG/whisper_large_v2_fp16_transformers
Please try in jupyter notebook
# Import the Model
from whisper_large_v2_fp16_transformers import Model, load_audio, pad_or_trim
# Initilise the model
model = Model(
model_name_or_path='whisper_large_v2_fp16_transformers',
cuda_visible_device="0",
device='cuda',
)
# Load Audio
audio = load_audio('whisper_large_v2_fp16_transformers/test.wav')
audio = pad_or_trim(audio)
# Transcribe (First transcription takes time)
model.transcribe(audio)
Credits
It is fp16 version of openai/whisper-large-v2
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Evaluation results
- Test WER on LibriSpeech (clean)test set self-reported0.000
- Test MER on LibriSpeech (clean)test set self-reported0.000
- Test WIL on LibriSpeech (clean)test set self-reported0.000
- Test WIP on LibriSpeech (clean)test set self-reported0.000
- Test CER on LibriSpeech (clean)test set self-reported0.000
- Test WER on LibriSpeech (other)test set self-reported0.000
- Test MER on LibriSpeech (other)test set self-reported0.000
- Test WIL on LibriSpeech (other)test set self-reported0.000
- Test WIP on LibriSpeech (other)test set self-reported0.000
- Test CER on LibriSpeech (other)test set self-reported0.000