fix checkpoint name for wav2vec2 conformer (#18994)
* fix checkpoint name for wav2vec2 conformer Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
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@@ -24,8 +24,8 @@ from ...utils import logging
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logger = logging.get_logger(__name__)
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logger = logging.get_logger(__name__)
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WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"facebook/wav2vec2-conformer-large-rel-pos": (
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"facebook/wav2vec2-conformer-rel-pos-large": (
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"https://huggingface.co/facebook/wav2vec2-conformer-large-rel-pos/resolve/main/config.json"
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"https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large/resolve/main/config.json"
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),
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),
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}
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}
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@@ -35,7 +35,7 @@ class Wav2Vec2ConformerConfig(PretrainedConfig):
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This is the configuration class to store the configuration of a [`Wav2Vec2ConformerModel`]. It is used to
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This is the configuration class to store the configuration of a [`Wav2Vec2ConformerModel`]. It is used to
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instantiate an Wav2Vec2Conformer model according to the specified arguments, defining the model architecture.
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instantiate an Wav2Vec2Conformer model according to the specified arguments, defining the model architecture.
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Instantiating a configuration with the defaults will yield a similar configuration to that of the Wav2Vec2Conformer
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Instantiating a configuration with the defaults will yield a similar configuration to that of the Wav2Vec2Conformer
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[facebook/wav2vec2-conformer-large-rel-pos](https://huggingface.co/facebook/wav2vec2-conformer-large-rel-pos)
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[facebook/wav2vec2-conformer-rel-pos-large](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large)
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architecture.
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architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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@@ -195,10 +195,10 @@ class Wav2Vec2ConformerConfig(PretrainedConfig):
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```python
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```python
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>>> from transformers import Wav2Vec2ConformerModel, Wav2Vec2ConformerConfig
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>>> from transformers import Wav2Vec2ConformerModel, Wav2Vec2ConformerConfig
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>>> # Initializing a Wav2Vec2Conformer facebook/wav2vec2-conformer-large-rel-pos style configuration
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>>> # Initializing a Wav2Vec2Conformer facebook/wav2vec2-conformer-rel-pos-large style configuration
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>>> configuration = Wav2Vec2ConformerConfig()
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>>> configuration = Wav2Vec2ConformerConfig()
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>>> # Initializing a model from the facebook/wav2vec2-conformer-large-rel-pos style configuration
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>>> # Initializing a model from the facebook/wav2vec2-conformer-rel-pos-large style configuration
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>>> model = Wav2Vec2ConformerModel(configuration)
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>>> model = Wav2Vec2ConformerModel(configuration)
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>>> # Accessing the model configuration
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>>> # Accessing the model configuration
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@@ -80,7 +80,7 @@ _XVECTOR_EXPECTED_OUTPUT = 1.0
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WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
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WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"facebook/wav2vec2-conformer-large-rel-pos",
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"facebook/wav2vec2-conformer-rel-pos-large",
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# See all Wav2Vec2Conformer models at https://huggingface.co/models?filter=wav2vec2-conformer
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# See all Wav2Vec2Conformer models at https://huggingface.co/models?filter=wav2vec2-conformer
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]
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]
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@@ -1226,7 +1226,7 @@ WAV2VEC2_CONFORMER_INPUTS_DOCSTRING = r"""
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`attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask ==
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`attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask ==
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True`. For all models whose processor has `config.return_attention_mask == False`, such as
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True`. For all models whose processor has `config.return_attention_mask == False`, such as
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[wav2vec2_conformer-base](https://huggingface.co/facebook/wav2vec2-conformer-large-rel-pos),
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[wav2vec2-conformer-rel-pos-large](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large),
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`attention_mask` should **not** be passed to avoid degraded performance when doing batched inference. For
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`attention_mask` should **not** be passed to avoid degraded performance when doing batched inference. For
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such models `input_values` should simply be padded with 0 and passed without `attention_mask`. Be aware
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such models `input_values` should simply be padded with 0 and passed without `attention_mask`. Be aware
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that these models also yield slightly different results depending on whether `input_values` is padded or
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that these models also yield slightly different results depending on whether `input_values` is padded or
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