Paraformer-Large
Beachmark Results
AISHELL-1
- Decode config:
- Decode without CTC
- Decode without LM
AISHELL-2
- Decode config:
- Decode without CTC
- Decode without LM
| CER(%) |
Pretrain model |
Finetune model |
| dev_ios |
2.80 |
2.60 |
| test_android |
3.13 |
2.84 |
| test_ios |
2.85 |
2.82 |
| test_mic |
3.06 |
2.88 |
Wenetspeech
- Decode config:
- Decode without CTC
- Decode without LM
| testset |
CER(%) |
| dev |
3.57 |
| test |
6.97 |
| test_net |
6.74 |
SpeechIO TIOBE
- Decode config 1:
- Decode without CTC
- Decode without LM
- With text norm
- Decode config 2:
- Decode without CTC
- Decode with Transformer-LM
- LM weight: 0.15
- With text norm
| testset |
w/o LM |
w/ LM |
| SPEECHIO_ASR_ZH00001 |
0.49 |
0.35 |
| SPEECHIO_ASR_ZH00002 |
3.23 |
2.86 |
| SPEECHIO_ASR_ZH00003 |
1.13 |
0.80 |
| SPEECHIO_ASR_ZH00004 |
1.33 |
1.10 |
| SPEECHIO_ASR_ZH00005 |
1.41 |
1.18 |
| SPEECHIO_ASR_ZH00006 |
5.25 |
4.85 |
| SPEECHIO_ASR_ZH00007 |
5.51 |
4.97 |
| SPEECHIO_ASR_ZH00008 |
3.69 |
3.18 |
| SPEECHIO_ASR_ZH00009 |
3.02 |
2.78 |
| SPEECHIO_ASR_ZH000010 |
3.35 |
2.99 |
| SPEECHIO_ASR_ZH000011 |
1.54 |
1.25 |
| SPEECHIO_ASR_ZH000012 |
2.06 |
1.68 |
| SPEECHIO_ASR_ZH000013 |
2.57 |
2.25 |
| SPEECHIO_ASR_ZH000014 |
3.86 |
3.08 |
| SPEECHIO_ASR_ZH000015 |
3.34 |
2.67 |
Fine-tuning Results
Fine-tuning
- Train config:
- Training data: aishell-1
- Training info: lr 0.0002, dataset_type: small, batch bins 2000, 2 gpu, acc_grad 1, 20 epochs
- Decoding info: beam_size 1, average_num 10
| model |
dev cer(%) |
test cer(%) |
| Pretrain |
1.75 |
1.95 |
| Full-tuning |
1.62 |
1.78 |
- Train config:
- Training data: 16k sichuan dialect
- Training info: lr 0.0002, dataset_type: small, batch bins 2000, 2 gpu, acc_grad 1, 20 epochs
- Decoding info: beam_size 1, average_num 10
| model |
Training Data(h) |
common cer(%) |
sichuan cer(%) |
| Pretrain |
|
8.57 |
19.81 |
| Full-tuning |
50 |
8.8 |
12 |
| |
100 |
9.24 |
11.63 |
| |
200 |
9.82 |
10.47 |
| |
300 |
9.95 |
10.44 |
| |
1000 |
9.99 |
9.78 |
Lora Fine-tuning
- Train config:
- Training data: 16k sichuan dialect
- Training info: lr 0.0002, dataset_type: small, batch bins 2000, 2 gpu, acc_grad 1, 20 epochs
- Lora info: lora_bias: "all", lora_list ['q','v'], lora_rank:8, lora_alpha:16, lora_dropout:0.1
- Decoding info: beam_size 1, average_num 10
| model |
Training Data(h) |
Trainable Parameters(M) |
Memory Usage(G) |
common cer(%) |
sichuan cer(%) |
| Pretrain |
|
|
|
8.57 |
19.81 |
| Full-tuning |
50 |
220.9 |
15 |
8.8 |
12 |
| Lora Finetune |
50 |
2.29 |
7 |
9.13 |
12.13 |
| Full-tuning |
200 |
220.9 |
15 |
9.82 |
10.47 |
| Lora Finetune |
200 |
2.29 |
7 |
9.21 |
11.28 |
