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---
license: apache-2.0
pipeline_tag: automatic-speech-recognition
tags:
- pytorch
- audio
- speech
- automatic-speech-recognition
- whisper
- wav2vec2
model-index:
- name: whisper_medium_fp16_transformers
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
type: librispeech_asr
name: LibriSpeech (clean)
config: clean
split: test
args:
language: en
metrics:
- type: wer
value: 0
name: Test WER
description: Word Error Rate
- type: mer
value: 0
name: Test MER
description: Match Error Rate
- type: wil
value: 0
name: Test WIL
description: Word Information Lost
- type: wip
value: 0
name: Test WIP
description: Word Information Preserved
- type: cer
value: 0
name: Test CER
description: Character Error Rate
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
type: librispeech_asr
name: LibriSpeech (other)
config: other
split: test
args:
language: en
metrics:
- type: wer
value: 0
name: Test WER
description: Word Error Rate
- type: mer
value: 0
name: Test MER
description: Match Error Rate
- type: wil
value: 0
name: Test WIL
description: Word Information Lost
- type: wip
value: 0
name: Test WIP
description: Word Information Preserved
- type: cer
value: 0
name: Test CER
description: Character Error Rate
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
type: mozilla-foundation/common_voice_14_0
name: Common Voice (14.0) (Hindi)
config: hi
split: test
args:
language: hi
metrics:
- type: wer
value: 54.97
name: Test WER
description: Word Error Rate
- type: mer
value: 47.86
name: Test MER
description: Match Error Rate
- type: wil
value: 66.83
name: Test WIL
description: Word Information Lost
- type: wip
value: 33.16
name: Test WIP
description: Word Information Preserved
- type: cer
value: 30.23
name: Test CER
description: Character Error Rate
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
language:
- en
- zh
- de
- es
- ru
- ko
- fr
- ja
- pt
- tr
- pl
- ca
- nl
- ar
- sv
- it
- id
- hi
- fi
- vi
- he
- uk
- el
- ms
- cs
- ro
- da
- hu
- ta
- 'no'
- th
- ur
- hr
- bg
- lt
- la
- mi
- ml
- cy
- sk
- te
- fa
- lv
- bn
- sr
- az
- sl
- kn
- et
- mk
- br
- eu
- is
- hy
- ne
- mn
- bs
- kk
- sq
- sw
- gl
- mr
- pa
- si
- km
- sn
- yo
- so
- af
- oc
- ka
- be
- tg
- sd
- gu
- am
- yi
- lo
- uz
- fo
- ht
- ps
- tk
- nn
- mt
- sa
- lb
- my
- bo
- tl
- mg
- as
- tt
- haw
- ln
- ha
- ba
- jw
- su
---
## 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: 2.8 GB (Original_Model: 5.5GB)
- VRAM: 1812 MB (Original_Model: 6GB)
- 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 | 1.7 | 1.1 | 3,584 | 112 |
| 1660 Super | OOM | 3.3 | 1,408 | N/A |
| Collab (Tesla T4) | 2.8 | 2.2 | 2,560 | 320 |
| Collab (CPU) | 35 | N/A | N/A | N/A |
| M1 (CPU) | - | - | - | - |
| M1 (GPU -> 'mps') | - | - | - | - |
- **NOTE: TensorCores are efficient in mixed-precision calculations**
- **CPU -> torch.float16 not supported on CPU (AMD Ryzen 5 3600 or Collab GPU)**
- Punchuation: True
## Model Error Benchmarks:
- **WER: Word Error Rate**
- **MER: Match Error Rate**
- **WIL: Word Information Lost**
- **WIP: Word Information Preserved**
- **CER: Character Error Rate**
### Hindi (test.tsv -> 2557 samples used) [Common Voice 14.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_14_0)
| | WER | MER | WIL | WIP | CER |
| ----------------------- | -------------------- | ------- | --------- | ----------- | ----- |
| Original_Model (54 min) | 52.02 | 47.86 | 66.82 | 33.17 | 23.76 |
| This_Model (38 min) | 54.97 | 47.86 | 66.83 | 33.16 | 30.23 |
### English (LibriSpeech -> test-clean -> __ samples used) [LibriSpeech](https://huggingface.co/datasets/librispeech_asr)
| | WER | MER | WIL | WIP | CER |
| ----------------- | -------------------- | ------- | --------- | ----------- | --- |
| Original_Model | - | - | - | - | - |
| This_Model | - | - | - | - | - |
### English (LibriSpeech -> test-other -> __ samples used) [LibriSpeech](https://huggingface.co/datasets/librispeech_asr)
| | WER | MER | WIL | WIP | CER |
| ----------------- | -------------------- | ------- | --------- | ----------- | --- |
| Original_Model | - | - | - | - | - |
| This_Model | - | - | - | - | - |
- **'jiwer' library is used for calculations**
## Code for conversion:
- ### [Will be soon Uploaded on Github](https://github.com/devasheeshG)
## 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_medium_fp16_transformers
```
**Please try in jupyter notebook**
```python
# Import the Model
from whisper_medium_fp16_transformers import Model
```
```python
# Initilise the model
model = Model(
model_name_or_path='whisper_medium_fp16_transformers',
cuda_visible_device="0",
device='cuda',
)
```
```python
# Load Audio
audio = model.load_audio('whisper_medium_fp16_transformers/test.wav')
```
```python
# Transcribe (First transcription takes time)
model.transcribe(audio)
``` |