File size: 8,046 Bytes
b289b78 |
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 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 |
---
license: apache-2.0
pipeline_tag: automatic-speech-recognition
tags:
- pytorch
- audio
- speech
- automatic-speech-recognition
- whisper
- wav2vec2
model-index:
- name: whisper_large_v2_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: 44.64
name: Test WER
description: Word Error Rate
- type: mer
value: 41.69
name: Test MER
description: Match Error Rate
- type: wil
value: 59.53
name: Test WIL
description: Word Information Lost
- type: wip
value: 40.46
name: Test WIP
description: Word Information Preserved
- type: cer
value: 16.80
name: Test CER
description: Character Error Rate
widget:
- example_title: Hinglish Sample
src: https://huggingface.co/devasheeshG/whisper_large_v2_fp16_transformers/resolve/main/test.wav
- 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: 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](https://commonvoice.mozilla.org/en/datasets)
**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](https://huggingface.co/datasets/devasheeshG/common_voices_14_0_hi2en_hi2hi)
**Test done on RTX 3060 on 1000 Samples**
| | WER | MER | WIL | WIP | CER |
| ----------------------- | --- | --- | --- | --- | --- |
| Original_Model (30 min) | - | - | - | - | - |
| This_Model (20 min) | - | - | - | - | - |
### English ([LibriSpeech](https://huggingface.co/datasets/librispeech_asr) -> test-clean)
**Test done on RTX 3060 on \_\_\_ Samples**
| | WER | MER | WIL | WIP | CER |
| -------------- | --- | --- | --- | --- | --- |
| Original_Model | - | - | - | - | - |
| This_Model | - | - | - | - | - |
### English ([LibriSpeech](https://huggingface.co/datasets/librispeech_asr) -> 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](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_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``
|