Instructions to use hf-tiny-model-private/tiny-random-Speech2TextForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Speech2TextForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-tiny-model-private/tiny-random-Speech2TextForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Speech2TextForConditionalGeneration") model = AutoModelForSpeechSeq2Seq.from_pretrained("hf-tiny-model-private/tiny-random-Speech2TextForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
File size: 1,077 Bytes
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"_name_or_path": "tiny_models/speech_to_text/Speech2TextForConditionalGeneration",
"activation_dropout": 0.0,
"activation_function": "relu",
"architectures": [
"Speech2TextForConditionalGeneration"
],
"attention_dropout": 0.1,
"bos_token_id": 0,
"conv_channels": 32,
"conv_kernel_sizes": [
5,
5
],
"d_model": 16,
"decoder_attention_heads": 4,
"decoder_ffn_dim": 4,
"decoder_layerdrop": 0.0,
"decoder_layers": 2,
"decoder_start_token_id": 2,
"dropout": 0.1,
"encoder_attention_heads": 4,
"encoder_ffn_dim": 4,
"encoder_layerdrop": 0.0,
"encoder_layers": 2,
"eos_token_id": 2,
"init_std": 0.02,
"input_channels": 1,
"input_feat_per_channel": 24,
"is_encoder_decoder": true,
"max_position_embeddings": 20,
"max_source_positions": 20,
"max_target_positions": 20,
"model_type": "speech_to_text",
"num_conv_layers": 2,
"num_hidden_layers": 2,
"pad_token_id": 1,
"scale_embedding": true,
"torch_dtype": "float32",
"transformers_version": "4.28.0.dev0",
"use_cache": true,
"vocab_size": 10000
}
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