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huggingface / distil-whisper

Distilled variant of Whisper for speech recognition. 6x faster, 50% smaller, within 1% word error rate.

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RuntimeError: Error(s) in loading state_dict for WhisperForConditionalGeneration.

Eskaggg opened this issue · comments

commented

Hi @sanchit-gandhi
I have followed the instruction to do the training but in the training section I get the below error. How to fix?
Traceback (most recent call last):
File "/home/codelex/Documents/lawly/linear_regression/distil-whisper/training/Distil-whisper-mn/create_student_model.py", line 206, in
init_student_model_from_teacher(
File "/home/codelex/Documents/lawly/linear_regression/distil-whisper/training/Distil-whisper-mn/create_student_model.py", line 134, in init_student_model_from_teacher
raise RuntimeError(
RuntimeError: Error(s) in loading state_dict for WhisperForConditionalGeneration.
Missing key(s) in state_dict: ['model.encoder.layers.4.self_attn.k_proj.weight', 'model.encoder.layers.4.self_attn.v_proj.weight', 'model.encoder.layers.4.self_attn.v_proj.bias', 'model.encoder.layers.4.self_attn.q_proj.weight', 'model.encoder.layers.4.self_attn.q_proj.bias', 'model.encoder.layers.4.self_attn.out_proj.weight', 'model.encoder.layers.4.self_attn.out_proj.bias', 'model.encoder.layers.4.self_attn_layer_norm.weight', 'model.encoder.layers.4.self_attn_layer_norm.bias', 'model.encoder.layers.4.fc1.weight', 'model.encoder.layers.4.fc1.bias', 'model.encoder.layers.4.fc2.weight', 'model.encoder.layers.4.fc2.bias', 'model.encoder.layers.4.final_layer_norm.weight', 'model.encoder.layers.4.final_layer_norm.bias', 'model.encoder.layers.5.self_attn.k_proj.weight', 'model.encoder.layers.5.self_attn.v_proj.weight', 'model.encoder.layers.5.self_attn.v_proj.bias', 'model.encoder.layers.5.self_attn.q_proj.weight', 'model.encoder.layers.5.self_attn.q_proj.bias', 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commented

Hey @Eskaggg, thanks for reporting! Could you confirm that you've made no changes to the file create_student_model.py? And subsequently, what the bash command is that you're using to run this script? e.g. like the template on the README:

#!/usr/bin/env bash

python create_student_model.py \
  --teacher_checkpoint "openai/whisper-large-v2" \
  --encoder_layers 32 \
  --decoder_layers 2 \
  --save_dir "./distil-large-v2-init"

And finally: could you please share the output of this command:

transformers-cli env

Many thanks!

commented

@sanchit-gandhi Tnks for reply
(output) transformers-cli env:
- transformers version: 4.35.2
- Platform: Linux-5.15.0-89-generic-x86_64-with-glibc2.31
- Python version: 3.10.13
- Huggingface_hub version: 0.19.4
- Safetensors version: 0.4.1
- Accelerate version: 0.25.0
- Accelerate config: not found
- PyTorch version (GPU?): 2.1.1+cu121 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?:
- Using distributed or parallel set-up in script?:

commented

And another error is here :

loading file vocab.json
loading file tokenizer.json
loading file merges.txt
loading file normalizer.json
loading file added_tokens.json
loading file special_tokens_map.json
loading file tokenizer_config.json
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
12/20/2023 11:49:18 - INFO - main - max_steps is given, it will override any value given in num_train_epochs
12/20/2023 11:49:19 - INFO - main - ***** Running training *****
12/20/2023 11:49:19 - INFO - main - Num examples = 40000
12/20/2023 11:49:19 - INFO - main - Instantaneous batch size per device = 8
12/20/2023 11:49:19 - INFO - main - Gradient accumulation steps = 1
12/20/2023 11:49:19 - INFO - main - Total train batch size (w. parallel & distributed) = 8
12/20/2023 11:49:19 - INFO - main - Total optimization steps = 5000
Train steps ... : 0%| | 0/5000 [00:00<?, ?it/s]Traceback (most recent call last):
File "/home/codelex/Documents/lawly/linear_regression/distil-whisper/training/Distil-whisper-mn/run_distillation.py", line 1631, in
main()
File "/home/codelex/Documents/lawly/linear_regression/distil-whisper/training/Distil-whisper-mn/run_distillation.py", line 1489, in main
loss, train_metric = train_step(batch, temperature=training_args.temperature)
File "/home/codelex/Documents/lawly/linear_regression/distil-whisper/training/Distil-whisper-mn/run_distillation.py", line 1357, in train_step
student_outputs = student_model(**batch)
TypeError: WhisperForConditionalGeneration(
(model): WhisperModel(
(encoder): WhisperEncoder(
(conv1): Conv1d(80, 1280, kernel_size=(3,), stride=(1,), padding=(1,))
(conv2): Conv1d(1280, 1280, kernel_size=(3,), stride=(2,), padding=(1,))
(embed_positions): Embedding(1500, 1280)
(layers): ModuleList(
(0-31): 32 x WhisperEncoderLayer(
(self_attn): WhisperAttention(
(k_proj): Linear(in_features=1280, out_features=1280, bias=False)
(v_proj): Linear(in_features=1280, out_features=1280, bias=True)
(q_proj): Linear(in_features=1280, out_features=1280, bias=True)
(out_proj): Linear(in_features=1280, out_features=1280, bias=True)
)
(self_attn_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(activation_fn): GELUActivation()
(fc1): Linear(in_features=1280, out_features=5120, bias=True)
(fc2): Linear(in_features=5120, out_features=1280, bias=True)
(final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
)
)
(layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
)
(decoder): WhisperDecoder(
(embed_tokens): Embedding(51865, 1280, padding_idx=50257)
(embed_positions): WhisperPositionalEmbedding(448, 1280)
(layers): ModuleList(
(0-1): 2 x WhisperDecoderLayer(
(self_attn): WhisperAttention(
(k_proj): Linear(in_features=1280, out_features=1280, bias=False)
(v_proj): Linear(in_features=1280, out_features=1280, bias=True)
(q_proj): Linear(in_features=1280, out_features=1280, bias=True)
(out_proj): Linear(in_features=1280, out_features=1280, bias=True)
)
(activation_fn): GELUActivation()
(self_attn_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(encoder_attn): WhisperAttention(
(k_proj): Linear(in_features=1280, out_features=1280, bias=False)
(v_proj): Linear(in_features=1280, out_features=1280, bias=True)
(q_proj): Linear(in_features=1280, out_features=1280, bias=True)
(out_proj): Linear(in_features=1280, out_features=1280, bias=True)
)
(encoder_attn_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=1280, out_features=5120, bias=True)
(fc2): Linear(in_features=5120, out_features=1280, bias=True)
(final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
)
)
(layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
)
)
(proj_out): Linear(in_features=1280, out_features=51865, bias=False)
) argument after ** must be a mapping, not NoneType

commented

Hey @Eskaggg - could you please give the command that you're using to initialise the student model, as requested previously? It would be super helpful if I could run this locally on my side to find the error. Thanks!