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  1. README.md +25 -0
  2. config.json +98 -0
  3. inference.py +10 -0
  4. openvino_model.bin +3 -0
  5. openvino_model.xml +0 -0
  6. preprocessor_config.json +13 -0
README.md ADDED
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+ ---
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+ language:
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+ - en
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+ tags:
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+ - openvino
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+ ---
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+
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+ # MIT/ast-finetuned-speech-commands-v2
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+
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+ This is the [MIT/ast-finetuned-speech-commands-v2](https://huggingface.co/MIT/ast-finetuned-speech-commands-v2) model converted to [OpenVINO](https://openvino.ai), for accellerated inference.
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+
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+ An example of how to do inference on this model:
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+ ```python
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+ from optimum.intel.openvino import OVModelForAudioClassification
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+ from transformers import AutoTokenizer, pipeline
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+
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+ # model_id should be set to either a local directory or a model available on the HuggingFace hub.
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+ model_id = "helenai/MIT-ast-finetuned-speech-commands-v2-ov"
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+ feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
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+ model = OVModelForAudioClassification.from_pretrained(model_id)
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+ pipe = pipeline("audio-classification", model=model, feature_extractor=feature_extractor)
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+ result = pipe("https://datasets-server.huggingface.co/assets/speech_commands/--/v0.01/test/38/audio/audio.mp3")
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+ print(result)
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+ ```
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+
config.json ADDED
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+ {
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+ "_name_or_path": "MIT/ast-finetuned-speech-commands-v2",
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+ "architectures": [
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+ "ASTForAudioClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.0,
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+ "frequency_stride": 10,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.0,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "backward",
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+ "1": "follow",
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+ "2": "five",
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+ "3": "bed",
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+ "4": "zero",
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+ "5": "on",
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+ "6": "learn",
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+ "7": "two",
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+ "8": "house",
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+ "9": "tree",
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+ "10": "dog",
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+ "11": "stop",
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+ "12": "seven",
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+ "13": "eight",
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+ "14": "down",
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+ "15": "six",
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+ "16": "forward",
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+ "17": "cat",
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+ "18": "right",
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+ "19": "visual",
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+ "20": "four",
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+ "21": "wow",
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+ "22": "no",
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+ "23": "nine",
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+ "24": "off",
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+ "25": "three",
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+ "26": "left",
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+ "27": "marvin",
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+ "28": "yes",
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+ "29": "up",
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+ "30": "sheila",
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+ "31": "happy",
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+ "32": "bird",
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+ "33": "go",
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+ "34": "one"
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+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "label2id": {
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+ "backward": 0,
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+ "bed": 3,
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+ "bird": 32,
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+ "cat": 17,
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+ "dog": 10,
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+ "down": 14,
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+ "eight": 13,
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+ "five": 2,
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+ "follow": 1,
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+ "forward": 16,
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+ "four": 20,
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+ "go": 33,
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+ "happy": 31,
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+ "house": 8,
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+ "learn": 6,
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+ "left": 26,
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+ "marvin": 27,
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+ "nine": 23,
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+ "no": 22,
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+ "off": 24,
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+ "on": 5,
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+ "one": 34,
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+ "right": 18,
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+ "seven": 12,
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+ "sheila": 30,
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+ "six": 15,
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+ "stop": 11,
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+ "three": 25,
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+ "tree": 9,
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+ "two": 7,
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+ "up": 29,
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+ "visual": 19,
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+ "wow": 21,
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+ "yes": 28,
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+ "zero": 4
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "max_length": 128,
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+ "model_type": "audio-spectrogram-transformer",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "num_mel_bins": 128,
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+ "patch_size": 16,
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+ "qkv_bias": true,
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+ "time_stride": 10,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.26.1"
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+ }
inference.py ADDED
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+ from optimum.intel.openvino import OVModelForAudioClassification
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+ from transformers import AutoTokenizer, pipeline
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+
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+ # model_id should be set to either a local directory or a model available on the HuggingFace hub.
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+ model_id = "helenai/MIT-ast-finetuned-speech-commands-v2-ov"
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+ feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
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+ model = OVModelForAudioClassification.from_pretrained(model_id)
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+ pipe = pipeline("audio-classification", model=model, feature_extractor=feature_extractor)
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+ result = pipe("https://datasets-server.huggingface.co/assets/speech_commands/--/v0.01/test/38/audio/audio.mp3")
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+ print(result)
openvino_model.bin ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:89a88fc7b9be32a3c8e60ad6738d24f33ee1dbc4e611a3d1b8bee5a4ef7d4095
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+ size 170794314
openvino_model.xml ADDED
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preprocessor_config.json ADDED
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+ {
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+ "do_normalize": true,
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+ "feature_extractor_type": "ASTFeatureExtractor",
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+ "feature_size": 1,
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+ "max_length": 128,
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+ "mean": -6.845978,
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+ "num_mel_bins": 128,
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+ "padding_side": "right",
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+ "padding_value": 0.0,
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+ "return_attention_mask": false,
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+ "sampling_rate": 16000,
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+ "std": 5.5654526
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+ }