You can deploy this model and start earning money today!

devasheeshG/
whisper_large_v2_fp16_transformers

Name: whisper_large_v2_fp16_transformers
Availability: InStock

transformers

pytorch

whisper

automatic-speech-recognition

audio

speech

wav2vec2

Views

No views yet

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:

Will be soon Uploaded on Github

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)

bash

git lfs install
git clone https://huggingface.co/devasheeshG/whisper_large_v2_fp16_transformers

Please try in jupyter notebook

python

# Import the Model
from whisper_large_v2_fp16_transformers import Model, load_audio, pad_or_trim

python

# Initilise the model
model = Model(
            model_name_or_path='whisper_large_v2_fp16_transformers',
            cuda_visible_device="0",
            device='cuda',
      )

python

# Load Audio
audio = load_audio('whisper_large_v2_fp16_transformers/test.wav')
audio = pad_or_trim(audio)

python

# Transcribe (First transcription takes time)
model.transcribe(audio)

Credits

It is fp16 version of openai/whisper-large-v2

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

devasheeshG/whisper_large_v2_fp16_transformers

Versions:

Model Benchmarks:

Model Error Benchmarks:

Hindi to Hindi (test.tsv) Common Voice 14.0

Hindi to English (test.csv) Custom Dataset

English (LibriSpeech -> test-clean)

English (LibriSpeech -> test-other)

Code for conversion:

Will be soon Uploaded on Github

Usage

Make sure you have git-lfs installed (https://git-lfs.com)

Credits

devasheeshG/
whisper_large_v2_fp16_transformers