xlsr model running issue

[REETICHI]

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I am trying to run xls-r model but i am facing issues while running it.Please help me resolve this issue.

Code

PYTHONPATH=. python examples/wav2vec/xlsr/scripts/gen_audio_embedding.py /mnt/sda/Reetichi --path /mnt/sda/Reetichi/xlsr_300m_voxlingua107_ft.pt --task audio_classification --infer-manifest /mnt/sda/Reetichi/infer.tsv --gen-subset test --output-path /mnt/sda/Reetichi/out.npz --labels lang
| loading model from /mnt/sda/Reetichi/xlsr_300m_voxlingua107_ft.pt
Wav2Vec2Model(
(feature_extractor): ConvFeatureExtractionModel(
(conv_layers): ModuleList(
(0): Sequential(
(0): Conv1d(1, 512, kernel_size=(10,), stride=(5,))
(1): Dropout(p=0.0, inplace=False)
(2): Sequential(
(0): TransposeLast()
(1): Fp32LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(2): TransposeLast()
)
(3): GELU(approximate='none')
)
(1): Sequential(
(0): Conv1d(512, 512, kernel_size=(3,), stride=(2,))
(1): Dropout(p=0.0, inplace=False)
(2): Sequential(
(0): TransposeLast()
(1): Fp32LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(2): TransposeLast()
)
(3): GELU(approximate='none')
)
(2): Sequential(
(0): Conv1d(512, 512, kernel_size=(3,), stride=(2,))
(1): Dropout(p=0.0, inplace=False)
(2): Sequential(
(0): TransposeLast()
(1): Fp32LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(2): TransposeLast()
)
(3): GELU(approximate='none')
)
(3): Sequential(
(0): Conv1d(512, 512, kernel_size=(3,), stride=(2,))
(1): Dropout(p=0.0, inplace=False)
(2): Sequential(
(0): TransposeLast()
(1): Fp32LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(2): TransposeLast()
)
(3): GELU(approximate='none')
)
(4): Sequential(
(0): Conv1d(512, 512, kernel_size=(3,), stride=(2,))
(1): Dropout(p=0.0, inplace=False)
(2): Sequential(
(0): TransposeLast()
(1): Fp32LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(2): TransposeLast()
)
(3): GELU(approximate='none')
)
(5): Sequential(
(0): Conv1d(512, 512, kernel_size=(2,), stride=(2,))
(1): Dropout(p=0.0, inplace=False)
(2): Sequential(
(0): TransposeLast()
(1): Fp32LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(2): TransposeLast()
)
(3): GELU(approximate='none')
)
(6): Sequential(
(0): Conv1d(512, 512, kernel_size=(2,), stride=(2,))
(1): Dropout(p=0.0, inplace=False)
(2): Sequential(
(0): TransposeLast()
(1): Fp32LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(2): TransposeLast()
)
(3): GELU(approximate='none')
)
)
)
(post_extract_proj): Linear(in_features=512, out_features=1024, bias=True)
(dropout_input): Dropout(p=0.0, inplace=False)
(dropout_features): Dropout(p=0.0, inplace=False)
(quantizer): None
(project_q): None
(encoder): TransformerEncoder(
(pos_conv): Sequential(
(0): Conv1d(1024, 1024, kernel_size=(128,), stride=(1,), padding=(64,), groups=16)
(1): SamePad()
(2): GELU(approximate='none')
)
(layers): ModuleList(
(0): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(1): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(2): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(3): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(4): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(5): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(6): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(7): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(8): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(9): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(10): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(11): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(12): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(13): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(14): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(15): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(16): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(17): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(18): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(19): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(20): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(21): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(22): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(23): TransformerSentenceEncoderLayer(
(self_attn): MultiheadAttention(
(dropout_module): FairseqDropout()
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(dropout1): Dropout(p=0.0, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.0, inplace=False)
(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
)
(layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(final_proj): None
)
Traceback (most recent call last):
File "examples/wav2vec/xlsr/scripts/gen_audio_embedding.py", line 139, in
models, _model_args, task = checkpoint_utils.load_model_ensemble_and_task([args.path],
File "/mnt/sda/Reetichi/fairseq/fairseq/checkpoint_utils.py", line 502, in load_model_ensemble_and_task
model = task.build_model(cfg.model, from_checkpoint=True)
File "/mnt/sda/Reetichi/fairseq/fairseq/tasks/audio_pretraining.py", line 224, in build_model
model = super().build_model(model_cfg, from_checkpoint)
File "/mnt/sda/Reetichi/fairseq/fairseq/tasks/fairseq_task.py", line 355, in build_model
model = models.build_model(cfg, self, from_checkpoint)
File "/mnt/sda/Reetichi/fairseq/fairseq/models/init.py", line 106, in build_model
return model.build_model(cfg, task)
File "/mnt/sda/Reetichi/fairseq/fairseq/models/wav2vec/wav2vec2_classification.py", line 64, in build_model
pooling_layer = get_pooling_layer(
File "/mnt/sda/Reetichi/fairseq/fairseq/models/wav2vec/wav2vec2_classification.py", line 113, in get_pooling_layer
assert cfg.pooling == 'mean'
AssertionError

What have you tried?

What's your environment?

• fairseq Version: main (source build from GitHub)
• PyTorch Version: 2.x (conda install)
• OS: Ubuntu 22.04 LTS (Linux)
• How you installed fairseq: source install (git clone + pip install -e .)
• Build command used: pip install --editable .
• Python version: 3.8 (conda environment: xlsr)
• CUDA version: 11.8
• cuDNN version: 8.x
• GPU model: NVIDIA GPU (CUDA-enabled)
• GPU configuration: Single GPU setup (CUDA_VISIBLE_DEVICES=0)
• Any other relevant information: