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/usr/data/condor/execute/dir_32133/whisper_slu
WhisperSLU(
(model): PeftModel(
(base_model): LoraModel(
(model): WhisperModel(
(encoder): WhisperEncoder(
(conv1): Conv1d(80, 768, kernel_size=(3,), stride=(1,), padding=(1,))
(conv2): Conv1d(768, 768, kernel_size=(3,), stride=(2,), padding=(1,))
(embed_positions): Embedding(1500, 768)
(layers): ModuleList(
(0-11): 12 x WhisperEncoderLayer(
(self_attn): WhisperAttention(
(k_proj): Linear(in_features=768, out_features=768, bias=False)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(activation_fn): GELUActivation()
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
)
(layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(decoder): WhisperDecoder(
(embed_tokens): Embedding(51865, 768, padding_idx=50257)
(embed_positions): WhisperPositionalEmbedding(448, 768)
(layers): ModuleList(
(0-11): 12 x WhisperDecoderLayer(
(self_attn): WhisperAttention(
(k_proj): Linear(in_features=768, out_features=768, bias=False)
(v_proj): lora.Linear(
(base_layer): Linear(in_features=768, out_features=768, bias=True)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=768, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=768, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(q_proj): lora.Linear(
(base_layer): Linear(in_features=768, out_features=768, bias=True)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=768, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=768, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(activation_fn): GELUActivation()
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(encoder_attn): WhisperAttention(
(k_proj): Linear(in_features=768, out_features=768, bias=False)
(v_proj): lora.Linear(
(base_layer): Linear(in_features=768, out_features=768, bias=True)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=768, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=768, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(q_proj): lora.Linear(
(base_layer): Linear(in_features=768, out_features=768, bias=True)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=768, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=768, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
)
(layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
)
)
)
(proj_out): Linear(in_features=768, out_features=51865, bias=False)
(classifier): WhisperClassificationHead(
(embed_positions): WhisperPositionalEmbedding(448, 768)
(layers): ModuleList(
(0-1): 2 x WhisperEncoderLayer(
(self_attn): WhisperAttention(
(k_proj): Linear(in_features=768, out_features=768, bias=False)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(activation_fn): GELUActivation()
(fc1): Linear(in_features=768, out_features=2048, bias=True)
(fc2): Linear(in_features=2048, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
)
(layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(out_proj): Linear(in_features=768, out_features=37, bias=True)
(crf): ConditionalRandomField()
)
)
{'loss': 0.0467, 'learning_rate': 5e-05, 'epoch': 0.18}
{'loss': 0.0433, 'learning_rate': 4.994863481875841e-05, 'epoch': 0.36}
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{'loss': 0.0391, 'learning_rate': 4.9538978924776634e-05, 'epoch': 0.71}
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{'loss': 0.0381, 'learning_rate': 4.8726393675266716e-05, 'epoch': 1.07}
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{'loss': 0.037, 'learning_rate': 4.817291893365055e-05, 'epoch': 1.25}
{'loss': 0.0374, 'learning_rate': 4.752422169756048e-05, 'epoch': 1.43}
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{'loss': 0.0375, 'learning_rate': 4.678296760308474e-05, 'epoch': 1.61}
{'loss': 0.0374, 'learning_rate': 4.595220262229601e-05, 'epoch': 1.79}
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{'loss': 0.0379, 'learning_rate': 4.503534054669892e-05, 'epoch': 1.97}
{'loss': 0.0342, 'learning_rate': 4.4036148959228365e-05, 'epoch': 2.14}
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{'loss': 0.0345, 'learning_rate': 4.2958733752443195e-05, 'epoch': 2.32}
{'loss': 0.0362, 'learning_rate': 4.180752225653292e-05, 'epoch': 2.5}
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{'loss': 0.0351, 'learning_rate': 3.9302916503054246e-05, 'epoch': 2.86}
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{'loss': 0.0333, 'learning_rate': 3.5119583578059846e-05, 'epoch': 3.4}
{'loss': 0.0323, 'learning_rate': 3.363412636053269e-05, 'epoch': 3.57}
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{'loss': 0.0332, 'learning_rate': 3.211318966577581e-05, 'epoch': 3.75}
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{'loss': 0.0294, 'learning_rate': 3.217032396915265e-06, 'epoch': 7.51}
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{'loss': 0.03, 'learning_rate': 8.176284240242638e-07, 'epoch': 8.22}
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{'loss': 0.0307, 'learning_rate': 4.6102107522336403e-07, 'epoch': 8.4}
{'loss': 0.0296, 'learning_rate': 2.052496544188487e-07, 'epoch': 8.58}
{'eval_loss': 0.04443991929292679, 'eval_f1_score': 0.6117000392618768, 'eval_label_f1': 0.8378484491558698, 'eval_wer': 0.1084062233919034, 'eval_runtime': 455.2323, 'eval_samples_per_second': 2.197, 'eval_steps_per_second': 0.549, 'epoch': 8.58}
{'loss': 0.0288, 'learning_rate': 5.136518124159162e-08, 'epoch': 8.76}
{'loss': 0.0297, 'learning_rate': 0.0, 'epoch': 8.94}
{'eval_loss': 0.04443608224391937, 'eval_f1_score': 0.6098231827111984, 'eval_label_f1': 0.8369351669941061, 'eval_wer': 0.10856103413576902, 'eval_runtime': 456.0761, 'eval_samples_per_second': 2.193, 'eval_steps_per_second': 0.548, 'epoch': 8.94}
{'train_runtime': 46949.8286, 'train_samples_per_second': 13.632, 'train_steps_per_second': 0.106, 'train_loss': 0.03340700387954712, 'epoch': 8.94}
***** train metrics *****
epoch = 8.94
train_loss = 0.0334
train_runtime = 13:02:29.82
train_samples_per_second = 13.632
train_steps_per_second = 0.106
{'eval_loss': 0.04443608224391937, 'eval_f1_score': 0.6098231827111984, 'eval_label_f1': 0.8369351669941061, 'eval_wer': 0.10856103413576902, 'eval_runtime': 457.7019, 'eval_samples_per_second': 2.185, 'eval_steps_per_second': 0.546, 'epoch': 8.94}
***** eval metrics *****
epoch = 8.94
eval_f1_score = 0.6098
eval_label_f1 = 0.8369
eval_loss = 0.0444
eval_runtime = 0:07:37.70
eval_samples = 1000
eval_samples_per_second = 2.185
eval_steps_per_second = 0.546
eval_wer = 0.1086
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