xlsr model running issue

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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.

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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