wynehills-mimi-ASR

mimi/wynehills-mimi-ASR

wynehills-mimi-ASR is a automatic speech recognition model from mimi. Use it when you need speech turned into text. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Automatic Speech Recognition
Library: transformers
Model type: wav2vec2
Access: Public
Created: Mar 2, 2022
Updated: Nov 30, 2021
SHA: a2b5e20b

Model card

From the Hugging Face model README

wynehills-mimi-ASR

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.3822
Wer: 0.6309

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 8
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: 1000
num_epochs: 70
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	1.54	20	1.4018	0.6435
No log	3.08	40	1.4704	0.6593
No log	4.62	60	1.4898	0.6625
No log	6.15	80	1.4560	0.6404
No log	7.69	100	1.3822	0.6309
No log	9.23	120	1.3822	0.6309
No log	10.77	140	1.3822	0.6309
No log	12.31	160	1.3822	0.6309
No log	13.85	180	1.3822	0.6309
No log	15.38	200	1.3822	0.6309
No log	16.92	220	1.3822	0.6309
No log	18.46	240	1.3822	0.6309
No log	20.0	260	1.3822	0.6309
No log	21.54	280	1.3822	0.6309
No log	23.08	300	1.3822	0.6309
No log	24.62	320	1.3822	0.6309
No log	26.15	340	1.3822	0.6309
No log	27.69	360	1.3822	0.6309
No log	29.23	380	1.3822	0.6309
No log	30.77	400	1.3822	0.6309
No log	32.31	420	1.3822	0.6309
No log	33.85	440	1.3822	0.6309
No log	35.38	460	1.3822	0.6309
No log	36.92	480	1.3822	0.6309
0.0918	38.46	500	1.3822	0.6309
0.0918	40.0	520	1.3822	0.6309
0.0918	41.54	540	1.3822	0.6309
0.0918	43.08	560	1.3822	0.6309
0.0918	44.62	580	1.3822	0.6309
0.0918	46.15	600	1.3822	0.6309
0.0918	47.69	620	1.3822	0.6309
0.0918	49.23	640	1.3822	0.6309
0.0918	50.77	660	1.3822	0.6309
0.0918	52.31	680	1.3822	0.6309
0.0918	53.85	700	1.3822	0.6309
0.0918	55.38	720	1.3822	0.6309
0.0918	56.92	740	1.3822	0.6309
0.0918	58.46	760	1.3822	0.6309
0.0918	60.0	780	1.3822	0.6309
0.0918	61.54	800	1.3822	0.6309
0.0918	63.08	820	1.3822	0.6309
0.0918	64.62	840	1.3822	0.6309
0.0918	66.15	860	1.3822	0.6309
0.0918	67.69	880	1.3822	0.6309
0.0918	69.23	900	1.3822	0.6309

Framework versions

Transformers 4.11.3
Pytorch 1.10.0+cu111
Datasets 1.13.3
Tokenizers 0.10.3

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
Created: Mar 2, 2022
Updated: Nov 30, 2021