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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "_name_or_path": "openai/whisper-base",
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "apply_spec_augment": false,
+  "architectures": [
+    "WhisperForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_whisper_spkreg.WhisperSpkRegConfig"
+  },
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "classifier_proj_size": 256,
+  "d_model": 512,
+  "decoder_attention_heads": 8,
+  "decoder_ffn_dim": 2048,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 6,
+  "decoder_start_token_id": 50258,
+  "dropout": 0.0,
+  "easy_margin": false,
+  "encoder_attention_heads": 8,
+  "encoder_ffn_dim": 2048,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 6,
+  "eos_token_id": 50257,
+  "forced_decoder_ids": [
+    [
+      1,
+      50259
+    ],
+    [
+      2,
+      50359
+    ],
+    [
+      3,
+      50363
+    ]
+  ],
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "label_smoothing": 0.0,
+  "loss_fct": "cross_entropy",
+  "margin": 0.35,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "max_length": 448,
+  "max_source_positions": 1500,
+  "max_target_positions": 448,
+  "median_filter_width": 7,
+  "model_type": "whisper_spkreg",
+  "num_hidden_layers": 6,
+  "num_mel_bins": 80,
+  "pad_token_id": 50257,
+  "reduction": "mean",
+  "scale": 30.0,
+  "scale_embedding": false,
+  "suppress_tokens": [
+    1,
+    2,
+    7,
+    8,
+    9,
+    10,
+    14,
+    25,
+    26,
+    27,
+    28,
+    29,
+    31,
+    58,
+    59,
+    60,
+    61,
+    62,
+    63,
+    90,
+    91,
+    92,
+    93,
+    359,
+    503,
+    522,
+    542,
+    873,
+    893,
+    902,
+    918,
+    922,
+    931,
+    1350,
+    1853,
+    1982,
+    2460,
+    2627,
+    3246,
+    3253,
+    3268,
+    3536,
+    3846,
+    3961,
+    4183,
+    4667,
+    6585,
+    6647,
+    7273,
+    9061,
+    9383,
+    10428,
+    10929,
+    11938,
+    12033,
+    12331,
+    12562,
+    13793,
+    14157,
+    14635,
+    15265,
+    15618,
+    16553,
+    16604,
+    18362,
+    18956,
+    20075,
+    21675,
+    22520,
+    26130,
+    26161,
+    26435,
+    28279,
+    29464,
+    31650,
+    32302,
+    32470,
+    36865,
+    42863,
+    47425,
+    49870,
+    50254,
+    50258,
+    50358,
+    50359,
+    50360,
+    50361,
+    50362
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.2",
+  "use_cache": true,
+  "use_weighted_layer_sum": false,
+  "vocab_size": 51865
+}

configuration_whisper_spkreg.py

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+"""Whisper model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+# fmt: off
+NON_SPEECH_TOKENS = [
+    1, 2, 7, 8, 9, 10, 14, 25,
+    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
+    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
+    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
+    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
+    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
+    11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
+    17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
+    34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
+]
+NON_SPEECH_TOKENS_MULTI = [
+    1, 2, 7, 8, 9, 10, 14, 25,
+    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
+    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
+    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
+    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
+    7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
+    14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
+    22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
+    42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
+]
+# fmt: on
+
+
+class WhisperSpkRegConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`WhisperModel`]. It is used to instantiate a
+    Whisper model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the Whisper
+    [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 51865):
+            Vocabulary size of the Whisper model. Defines the number of different tokens that can be represented by the
+            `decoder_input_ids` passed when calling [`WhisperModel`]
+        num_mel_bins (`int`, *optional*, defaults to 80):
+            Number of mel features used per input features. Should correspond to the value used in the
+            `WhisperProcessor` class.
+        encoder_layers (`int`, *optional*, defaults to 4):
+            Number of encoder layers.
+        decoder_layers (`int`, *optional*, defaults to 4):
+            Number of decoder layers.
+        encoder_attention_heads (`int`, *optional*, defaults to 6):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        decoder_attention_heads (`int`, *optional*, defaults to 6):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        encoder_ffn_dim (`int`, *optional*, defaults to 1536):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in encoder.
+        decoder_ffn_dim (`int`, *optional*, defaults to 1536):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+        encoder_layerdrop (`float`, *optional*, defaults to 0.0):
+            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
+            for more details.
+        decoder_layerdrop (`float`, *optional*, defaults to 0.0):
+            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
+            for more details.
+        decoder_start_token_id (`int`, *optional*, defaults to 50257):
+            Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids`
+            are provided to the `generate` function. It is used to guide the model`s generation process depending on
+            the task.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models).
+        is_encoder_decoder (`bool`, *optional*, defaults to `True`):
+            Whether the model is used as an encoder/decoder or not.
+        activation_function (`str`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"silu"` and `"gelu_new"` are supported.
+        d_model (`int`, *optional*, defaults to 384):
+            Dimensionality of the layers.
+        dropout (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        activation_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for activations inside the fully connected layer.
+        init_std (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        scale_embedding (`bool`, *optional*, defaults to False):
+            Scale embeddings by diving by sqrt(d_model).
+        max_source_positions (`int`, *optional*, defaults to 1500):
+            The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
+        max_target_positions (`int`, *optional*, defaults to 448):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        pad_token_id (`int`, *optional*, defaults to 50256):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 50256):
+            Begin of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 50256):
+            End of stream token id.
+        suppress_tokens (`List[int]`, *optional*):
+            A list containing the non-speech tokens that will be used by the logit processor in the `generate`
+            function. NON_SPEECH_TOKENS and NON_SPEECH_TOKENS_MULTI each correspond to the `english-only` and the
+            `multilingual` model.
+        begin_suppress_tokens (`List[int]`, *optional*, defaults to `[220,50256]`):
+            A list containing tokens that will be supressed at the beginning of the sampling process. Initialized as
+            the token for `" "` (`blank_token_id`) and the `eos_token_id`
+        use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
+            Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
+            instance of [`WhisperForAudioClassification`].
+        classifier_proj_size (`int`, *optional*, defaults to 256):
+            Dimensionality of the projection before token mean-pooling for classification. Only relevant when using an
+            instance of [`WhisperForAudioClassification`].
+        apply_spec_augment (`bool`, *optional*, defaults to `False`):
+            Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
+            [SpecAugment: A Simple Data Augmentation Method for Automatic Speech
+            Recognition](https://arxiv.org/abs/1904.08779).
+        mask_time_prob (`float`, *optional*, defaults to 0.05):
+            Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
+            procecure generates `mask_time_prob*len(time_axis)/mask_time_length` independent masks over the axis. If
+            reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
+            masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
+            actual percentage of masked vectors. This is only relevant if `apply_spec_augment == True`.
+        mask_time_length (`int`, *optional*, defaults to 10):
+            Length of vector span along the time axis.
+        mask_time_min_masks (`int`, *optional*, defaults to 2),:
+            The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
+            irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
+            mask_time_min_masks''
+        mask_feature_prob (`float`, *optional*, defaults to 0.0):
+            Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
+            masking procecure generates `mask_feature_prob*len(feature_axis)/mask_time_length` independent masks over
+            the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
+            span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
+            may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
+            True`.
+        mask_feature_length (`int`, *optional*, defaults to 10):
+            Length of vector span along the feature axis.
+        mask_feature_min_masks (`int`, *optional*, defaults to 0),:
+            The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
+            step, irrespectively of `mask_feature_prob`. Only relevant if
+            `mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks`.
+        median_filter_width (`int`, *optional*, defaults to 7):
+            Width of the median filter used to smoothen to cross-attention outputs when computing token timestamps.
+            Should be an odd number.
+
+    Example:
+
+    ```python
+    >>> from transformers import WhisperConfig, WhisperModel
+
+    >>> # Initializing a Whisper tiny style configuration
+    >>> configuration = WhisperConfig()
+
+    >>> # Initializing a model (with random weights) from the tiny style configuration
+    >>> model = WhisperModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "whisper_spkreg"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {
+        "num_key_value_heads": "encoder_attention_heads",
+        "num_attention_heads": "encoder_attention_heads",
+        "hidden_size": "d_model",
+    }
+
+    def __init__(
+        self,
+        vocab_size=51865,
+        num_mel_bins=80,
+        encoder_layers=4,
+        encoder_attention_heads=6,
+        decoder_layers=4,
+        decoder_attention_heads=6,
+        decoder_ffn_dim=1536,
+        encoder_ffn_dim=1536,
+        encoder_layerdrop=0.0,
+        decoder_layerdrop=0.0,
+        decoder_start_token_id=50257,
+        use_cache=True,
+        is_encoder_decoder=True,
+        activation_function="gelu",
+        d_model=384,
+        dropout=0.0,
+        attention_dropout=0.0,
+        activation_dropout=0.0,
+        init_std=0.02,
+        scale_embedding=False,
+        max_source_positions=1500,
+        max_target_positions=448,
+        pad_token_id=50256,
+        bos_token_id=50256,
+        eos_token_id=50256,
+        suppress_tokens=None,
+        begin_suppress_tokens=[220, 50256],
+        use_weighted_layer_sum=False,
+        classifier_proj_size=256,
+        apply_spec_augment=False,
+        mask_time_prob=0.05,
+        mask_time_length=10,
+        mask_time_min_masks=2,
+        mask_feature_prob=0.0,
+        mask_feature_length=10,
+        mask_feature_min_masks=0,
+        median_filter_width=7,
+        loss_fct: str = 'cross_entropy', # cross_entropy, additive_margin, additive_angular_margin
+        label_smoothing: float = 0.0, 
+        scale: float = 30.0, 
+        margin: float = 0.35, 
+        easy_margin: bool = False, 
+        reduction: str = "mean", 
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.num_mel_bins = num_mel_bins
+        self.d_model = d_model
+        self.encoder_layers = encoder_layers
+        self.encoder_attention_heads = encoder_attention_heads
+        self.decoder_layers = decoder_layers
+        self.decoder_attention_heads = decoder_attention_heads
+        self.decoder_ffn_dim = decoder_ffn_dim
+        self.encoder_ffn_dim = encoder_ffn_dim
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.activation_dropout = activation_dropout
+        self.activation_function = activation_function
+        self.init_std = init_std
+        self.encoder_layerdrop = encoder_layerdrop
+        self.decoder_layerdrop = decoder_layerdrop
+        self.use_cache = use_cache
+        self.num_hidden_layers = encoder_layers
+        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True
+        self.max_source_positions = max_source_positions
+        self.max_target_positions = max_target_positions
+
+        # Audio Classification-specific parameters. Feel free to ignore for other classes.
+        self.classifier_proj_size = classifier_proj_size
+        self.use_weighted_layer_sum = use_weighted_layer_sum
+
+        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
+        self.apply_spec_augment = apply_spec_augment
+        self.mask_time_prob = mask_time_prob
+        self.mask_time_length = mask_time_length
+        self.mask_time_min_masks = mask_time_min_masks
+        self.mask_feature_prob = mask_feature_prob
+        self.mask_feature_length = mask_feature_length
+        self.mask_feature_min_masks = mask_feature_min_masks
+
+        self.median_filter_width = median_filter_width
+
+        # Loss function parameters. Feel free to ignore for other classes.
+        self.loss_fct = loss_fct
+        self.label_smoothing = label_smoothing
+        self.scale = scale
+        self.margin = margin
+        self.easy_margin = easy_margin
+        self.reduction = reduction
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            is_encoder_decoder=is_encoder_decoder,
+            decoder_start_token_id=decoder_start_token_id,
+            suppress_tokens=suppress_tokens,
+            begin_suppress_tokens=begin_suppress_tokens,
+            **kwargs,
+        )