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ModelZoo
fairseq-wav2vec_pytorch
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68758ee6
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68758ee6
authored
Nov 25, 2024
by
wangwei990215
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@@ -99,13 +99,12 @@ python examples/wav2vec/wav2vec_manifest.py /path/to/waves --dest /manifest/path
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@@ -99,13 +99,12 @@ python examples/wav2vec/wav2vec_manifest.py /path/to/waves --dest /manifest/path
```
```
-
训练 wav2vec 模型
-
训练 wav2vec 模型
```
```
export HIP_VISIBLE_DEVICES=0
python fairseq/train.py manifest/ --save-dir model-save/ --num-workers 6 --fp16 --max-update 400000 --save-interval 1 --no-epoch-checkpoints \
python train.py /manifest/path --save-dir /model/path --num-workers 6 --fp16 --max-update 400000 --save-interval 1 --no-epoch-checkpoints \
--arch wav2vec --task audio_pretraining --lr 1e-06 --min-lr 1e-09 --optimizer adam --max-lr 0.005 --lr-scheduler cosine \
--arch wav2vec --task audio_pretraining --min-lr 1e-06 --stop-min-lr 1e-09 --optimizer adam --lr 0.005 --lr-scheduler cosine \
--conv-feature-layers "[(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1)]" \
--conv-feature-layers [(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1)] \
--conv-aggregator-layers "[(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)]" \
--conv-aggregator-layers [(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)] \
--skip-connections-agg --residual-scale 0.5 --log-compression --warmup-updates 500 --warmup-init-lr 1e-07 --criterion wav2vec --num-negatives 10 \
--skip-connections-agg --residual-scale 0.5 --log-compression --warmup-updates 500 --warmup-init-lr 1e-07 --criterion wav2vec --num-negatives 10 \
--max-sample-size 150000 --
batch-size 16
--skip-invalid-size-inputs-valid-test
--device-id 1
--max-sample-size 150000 --
max-tokens 1500000
--skip-invalid-size-inputs-valid-test
```
```
-
HIP_VISIBLE_DEVICES:用于训练的卡的序号
-
HIP_VISIBLE_DEVICES:用于训练的卡的序号
...
...
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