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tests/models/cohere_asr/test_modeling_cohere_asr.py

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+# Copyright 2026 the HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+import math
+import unittest
+
+from transformers import AutoProcessor, CohereAsrConfig, CohereAsrForConditionalGeneration, is_torch_available
+from transformers.audio_utils import load_audio
+from transformers.testing_utils import Expectations, cleanup, require_torch, slow, torch_device
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    GenerationTesterMixin,
+    ModelTesterMixin,
+    floats_tensor,
+    random_attention_mask,
+)
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import CohereAsrForConditionalGeneration
+    from transformers.models.cohere_asr.modeling_cohere_asr import CohereAsrModel
+
+
+class CohereAsrModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=3,
+        seq_length=256,
+        is_training=False,
+        encoder_config={
+            "model_type": "parakeet_encoder",
+            "hidden_size": 16,
+            "intermediate_size": 32,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 2,
+            "hidden_act": "silu",
+            "attention_bias": True,
+            "convolution_bias": True,
+            "conv_kernel_size": 9,
+            "subsampling_factor": 4,
+            "subsampling_conv_channels": 8,
+            "num_mel_bins": 8,
+            "subsampling_conv_kernel_size": 3,
+            "subsampling_conv_stride": 2,
+            "dropout": 0.0,
+            "dropout_positions": 0.0,
+            "layerdrop": 0.0,
+            "activation_dropout": 0.0,
+            "attention_dropout": 0.0,
+            "max_position_embeddings": 5000,
+            "scale_input": False,
+        },
+        decoder_start_token_id=85,
+        bos_token_id=98,
+        eos_token_id=98,
+        pad_token_id=0,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.encoder_config = encoder_config
+        self.decoder_start_token_id = decoder_start_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.pad_token_id = pad_token_id
+
+        # Decoder defaults
+        self.vocab_size = 147
+        self.hidden_size = 16
+        self.intermediate_size = 32
+        self.num_hidden_layers = 2
+        self.num_attention_heads = 2
+        self.num_key_value_heads = 2
+        self.head_dim = 8
+
+        # Derived from encoder_config for test assertions
+        self.num_mel_bins = encoder_config["num_mel_bins"]
+        self.encoder_hidden_size = encoder_config["hidden_size"]
+        self.encoder_num_hidden_layers = encoder_config["num_hidden_layers"]
+        self.encoder_num_attention_heads = encoder_config["num_attention_heads"]
+        self.encoder_seq_length = self.get_encoder_output_length(seq_length)
+        self.decoder_seq_length = 1
+        self.decoder_key_length = 1
+        self.key_length = self.encoder_seq_length
+
+    def get_encoder_output_length(self, input_length):
+        """Compute the encoder output length after subsampling convolutions."""
+        num_layers = int(math.log2(self.encoder_config["subsampling_factor"]))
+        kernel_size = self.encoder_config["subsampling_conv_kernel_size"]
+        stride = self.encoder_config["subsampling_conv_stride"]
+        add_pad = (kernel_size - 1) // 2 * 2 - kernel_size
+        length = input_length
+        for _ in range(num_layers):
+            length = int((length + add_pad) / stride) + 1
+        return length
+
+    def get_config(self):
+        return CohereAsrConfig(
+            encoder_config=self.encoder_config,
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            intermediate_size=self.intermediate_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            num_key_value_heads=self.num_key_value_heads,
+            head_dim=self.head_dim,
+            hidden_act="relu",
+            attention_bias=True,
+            attention_dropout=0.0,
+            decoder_start_token_id=self.decoder_start_token_id,
+            bos_token_id=self.bos_token_id,
+            eos_token_id=self.eos_token_id,
+            pad_token_id=self.pad_token_id,
+        )
+
+    def prepare_config_and_inputs(self):
+        input_features = floats_tensor([self.batch_size, self.seq_length, self.num_mel_bins], scale=1.0)
+        attention_mask = random_attention_mask([self.batch_size, self.seq_length])
+        decoder_input_ids = torch.tensor(self.batch_size * [[self.decoder_start_token_id]], device=torch_device)
+        decoder_attention_mask = decoder_input_ids.ne(self.pad_token_id)
+        config = self.get_config()
+        return config, input_features, attention_mask, decoder_input_ids, decoder_attention_mask
+
+    def prepare_config_and_inputs_for_common(self):
+        config, input_features, attention_mask, decoder_input_ids, decoder_attention_mask = (
+            self.prepare_config_and_inputs()
+        )
+        inputs_dict = {
+            "input_features": input_features,
+            "decoder_input_ids": decoder_input_ids,
+            "decoder_attention_mask": decoder_attention_mask,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class CohereAsrModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (CohereAsrModel, CohereAsrForConditionalGeneration) if is_torch_available() else ()
+    all_generative_model_classes = (CohereAsrForConditionalGeneration,)
+    pipeline_model_mapping = (
+        {
+            "automatic-speech-recognition": CohereAsrForConditionalGeneration,
+            "feature-extraction": CohereAsrModel,
+        }
+        if is_torch_available()
+        else {}
+    )
+    is_encoder_decoder = True
+
+    # CohereAsr's pos_emb layer is large relative to total model size
+    model_split_percents = [0.5, 0.9, 0.95]
+
+    def setUp(self):
+        self.model_tester = CohereAsrModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=CohereAsrConfig)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_reverse_loading_mapping(self):
+        # proj_out conversion only applies to ForConditionalGeneration, not the base model
+        super().test_reverse_loading_mapping(skip_base_model=True)
+
+    # Copied from tests.models.moonshine_streaming.test_modeling_moonshine_streaming.MoonshineStreamingModelTest.test_resize_tokens_embeddings
+    def test_resize_tokens_embeddings(self):
+        (
+            original_config,
+            inputs_dict,
+        ) = self.model_tester.prepare_config_and_inputs_for_common()
+        if not self.test_resize_embeddings:
+            self.skipTest(reason="test_resize_embeddings is False")
+
+        for model_class in self.all_model_classes:
+            config = copy.deepcopy(original_config)
+            model = model_class(config)
+            model.to(torch_device)
+
+            if self.model_tester.is_training is False:
+                model.eval()
+
+            model_vocab_size = config.vocab_size
+            # Retrieve the embeddings and clone theme
+            model_embed = model.resize_token_embeddings(model_vocab_size)
+            cloned_embeddings = model_embed.weight.clone()
+
+            # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
+            model_embed = model.resize_token_embeddings(model_vocab_size + 10)
+            self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
+            # Check that it actually resizes the embeddings matrix
+            self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
+            # Check that the model can still do a forward pass successfully (every parameter should be resized)
+            model(**self._prepare_for_class(inputs_dict, model_class))
+
+            # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
+            model_embed = model.resize_token_embeddings(model_vocab_size - 15)
+            self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
+            # Check that it actually resizes the embeddings matrix
+            self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
+
+            # make sure that decoder_input_ids are resized
+            if "decoder_input_ids" in inputs_dict:
+                inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
+            model(**self._prepare_for_class(inputs_dict, model_class))
+
+            # Check that adding and removing tokens has not modified the first part of the embedding matrix.
+            models_equal = True
+            for p1, p2 in zip(cloned_embeddings, model_embed.weight):
+                if p1.data.ne(p2.data).sum() > 0:
+                    models_equal = False
+
+            self.assertTrue(models_equal)
+
+    # Copied from tests.models.moonshine_streaming.test_modeling_moonshine_streaming.MoonshineStreamingModelTest.test_resize_embeddings_untied
+    def test_resize_embeddings_untied(self):
+        (
+            original_config,
+            inputs_dict,
+        ) = self.model_tester.prepare_config_and_inputs_for_common()
+        if not self.test_resize_embeddings:
+            self.skipTest(reason="test_resize_embeddings is False")
+
+        original_config.tie_word_embeddings = False
+
+        # if model cannot untied embeddings -> leave test
+        if original_config.tie_word_embeddings:
+            self.skipTest(reason="Model cannot untie embeddings")
+
+        for model_class in self.all_model_classes:
+            config = copy.deepcopy(original_config)
+            model = model_class(config).to(torch_device)
+            model.eval()
+
+            # if no output embeddings -> leave test
+            if model.get_output_embeddings() is None:
+                continue
+
+            # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
+            model_vocab_size = config.vocab_size
+            model.resize_token_embeddings(model_vocab_size + 10)
+            self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
+            output_embeds = model.get_output_embeddings()
+            self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
+            # Check bias if present
+            if output_embeds.bias is not None:
+                self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
+            # Check that the model can still do a forward pass successfully (every parameter should be resized)
+            model(**self._prepare_for_class(inputs_dict, model_class))
+
+            # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
+            model.resize_token_embeddings(model_vocab_size - 15)
+            self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
+            # Check that it actually resizes the embeddings matrix
+            output_embeds = model.get_output_embeddings()
+            self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
+            # Check bias if present
+            if output_embeds.bias is not None:
+                self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
+            # Check that the model can still do a forward pass successfully (every parameter should be resized)
+            if "decoder_input_ids" in inputs_dict:
+                inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
+            # Check that the model can still do a forward pass successfully (every parameter should be resized)
+            model(**self._prepare_for_class(inputs_dict, model_class))
+
+    @unittest.skip(reason="Not known - aborted for now, not super important")
+    def test_sdpa_can_dispatch_on_flash(self):
+        pass
+
+    @unittest.skip(reason="FIXME: likely intended because we need input ids but to double-check")
+    def test_generate_without_input_ids(self):
+        pass
+
+
+# TODO: remove revision
+@require_torch
+class CohereAsrIntegrationTest(unittest.TestCase):
+    checkpoint_name = "CohereLabs/cohere-transcribe-03-2026"
+
+    def setUp(self):
+        self.processor = AutoProcessor.from_pretrained(self.checkpoint_name, revision="refs/pr/6")
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @slow
+    def test_shortform_english(self):
+        """
+        reproducer: https://gist.github.com/eustlb/cfcea58b4ffabfd45b4b6fce5ab283ed
+        """
+        audio = load_audio(
+            "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3",
+            sampling_rate=16000,
+        )
+        inputs = self.processor(
+            audio,
+            sampling_rate=16000,
+            return_tensors="pt",
+            language="en",
+        )
+        model = CohereAsrForConditionalGeneration.from_pretrained(
+            self.checkpoint_name, device_map=torch_device, revision="refs/pr/6"
+        )
+        inputs.to(model.device, dtype=model.dtype)
+
+        outputs = model.generate(**inputs, max_new_tokens=256)
+        text = self.processor.decode(outputs, skip_special_tokens=True)
+
+        EXPECTED_OUTPUT = Expectations(
+            {
+                ("xpu", None): [
+                    " Yesterday it was 35 degrees in Barcelona, but today the temperature will go down to minus 20 degrees."
+                ],
+                ("cuda", None): [
+                    " Yesterday it was 35 degrees in Barcelona, but today the temperature will go down to minus 20 degrees."
+                ],
+            }
+        ).get_expectation()
+        self.assertEqual(text, EXPECTED_OUTPUT)
+
+    @slow
+    def test_shortform_english_no_punctuation(self):
+        """
+        reproducer: https://gist.github.com/eustlb/cfcea58b4ffabfd45b4b6fce5ab283ed
+        """
+        audio = load_audio(
+            "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3",
+            sampling_rate=16000,
+        )
+        inputs_pnc = self.processor(audio, sampling_rate=16000, return_tensors="pt", language="en", punctuation=True)
+        inputs_nopnc = self.processor(
+            audio, sampling_rate=16000, return_tensors="pt", language="en", punctuation=False
+        )
+
+        model = CohereAsrForConditionalGeneration.from_pretrained(
+            self.checkpoint_name, device_map=torch_device, revision="refs/pr/6"
+        )
+        inputs_pnc.to(model.device, dtype=model.dtype)
+        inputs_nopnc.to(model.device, dtype=model.dtype)
+
+        outputs_pnc = model.generate(**inputs_pnc, max_new_tokens=256)
+        outputs_nopnc = model.generate(**inputs_nopnc, max_new_tokens=256)
+
+        text_pnc = self.processor.decode(outputs_pnc, skip_special_tokens=True)
+        text_nopnc = self.processor.decode(outputs_nopnc, skip_special_tokens=True)
+
+        EXPECTED_OUTPUT_PNC = Expectations(
+            {
+                ("xpu", None): [
+                    " Yesterday it was 35 degrees in Barcelona, but today the temperature will go down to minus 20 degrees."
+                ],
+                ("cuda", None): [
+                    " Yesterday it was 35 degrees in Barcelona, but today the temperature will go down to minus 20 degrees."
+                ],
+            }
+        ).get_expectation()
+        EXPECTED_OUTPUT_NOPNC = Expectations(
+            {
+                ("xpu", None): [
+                    " yesterday it was 35 degrees in barcelona but today the temperature will go down to minus 20 degrees"
+                ],
+                ("cuda", None): [
+                    " yesterday it was 35 degrees in barcelona but today the temperature will go down to minus 20 degrees"
+                ],
+            }
+        ).get_expectation()
+        self.assertEqual(text_pnc, EXPECTED_OUTPUT_PNC)
+        self.assertEqual(text_nopnc, EXPECTED_OUTPUT_NOPNC)
+
+    @slow
+    def test_longform_english(self):
+        """
+        reproducer: https://gist.github.com/eustlb/cfcea58b4ffabfd45b4b6fce5ab283ed
+        """
+        audio = load_audio(
+            "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama_first_45_secs.mp3",
+            sampling_rate=16000,
+        )
+        inputs = self.processor(audio=audio, return_tensors="pt", language="en", sampling_rate=16000)
+        audio_chunk_index = inputs.get("audio_chunk_index")
+        model = CohereAsrForConditionalGeneration.from_pretrained(
+            self.checkpoint_name, device_map=torch_device, revision="refs/pr/6"
+        )
+        inputs.to(model.device, dtype=model.dtype)
+
+        outputs = model.generate(**inputs, max_new_tokens=256)
+        text = self.processor.decode(
+            outputs, skip_special_tokens=True, audio_chunk_index=audio_chunk_index, language="en"
+        )
+
+        # fmt: off
+        EXPECTED_OUTPUT = [
+            " This week, I traveled to Chicago to deliver my final farewell address to the nation, following in the tradition of presidents before me. It was an opportunity to say thank you. Whether we've seen eye to eye or rarely agreed at all, my conversations with you, the American people, in living rooms and schools, at farms and on factory floors, at diners and on distant military outposts, all these conversations are what have kept me honest, kept me inspired, and kept me going. Every day I learned from you. You made me a better president and you made me a better man. Over the course of these eight years, I've seen the goodness, the resilience, and the hope of the American."
+        ]
+        # fmt: on
+        self.assertEqual(text, EXPECTED_OUTPUT)
+
+    @slow
+    def test_batched_mixed_lengths(self):
+        """
+        reproducer: https://gist.github.com/eustlb/cfcea58b4ffabfd45b4b6fce5ab283ed
+        """
+        audio_short = load_audio(
+            "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3",
+            sampling_rate=16000,
+        )
+        audio_long = load_audio(
+            "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama_first_45_secs.mp3",
+            sampling_rate=16000,
+        )
+        inputs = self.processor([audio_short, audio_long], sampling_rate=16000, return_tensors="pt", language="en")
+        audio_chunk_index = inputs.get("audio_chunk_index")
+        model = CohereAsrForConditionalGeneration.from_pretrained(
+            self.checkpoint_name, device_map=torch_device, revision="refs/pr/6"
+        )
+        inputs.to(model.device, dtype=model.dtype)
+
+        outputs = model.generate(**inputs, max_new_tokens=256)
+        text = self.processor.decode(
+            outputs, skip_special_tokens=True, audio_chunk_index=audio_chunk_index, language="en"
+        )
+
+        # fmt: off
+        EXPECTED_OUTPUT = Expectations(
+            {
+                ("xpu", None): [
+                    " Yesterday it was 35 degrees in Barcelona, but today the temperature will go down to minus 20 degrees.",
+                    " This week, I traveled to Chicago to deliver my final farewell address to the nation, following in the tradition of presidents before me. It was an opportunity to say thank you. Whether we've seen eye to eye or rarely agreed at all, my conversations with you, the American people, in living rooms and schools, at farms and on factory floors, at diners and on distant military outposts, all these conversations are what have kept me honest, kept me inspired, and kept me going. Every day I learned from you. You made me a better president and you made me a better man. Over the course of these eight years, I've seen the goodness, the resilience, and the hope of the American.",
+                ],
+                ("cuda", None): [
+                    " Yesterday it was 35 degrees in Barcelona, but today the temperature will go down to minus 20 degrees.",
+                    " This week, I traveled to Chicago to deliver my final farewell address to the nation, following in the tradition of presidents before me. It was an opportunity to say thank you. Whether we've seen eye to eye or rarely agreed at all, my conversations with you, the American people, in living rooms and schools, at farms and on factory floors, at diners and on distant military outposts, all these conversations are what have kept me honest, kept me inspired, and kept me going. Every day I learned from you. You made me a better president and you made me a better man. Over the course of these eight years, I've seen the goodness, the resilience, and the hope of the American."
+                ],
+            }
+        ).get_expectation()
+        # fmt: on
+        self.assertEqual(text, EXPECTED_OUTPUT)
+
+    @slow
+    def test_non_english_with_punctuation(self):
+        """
+        reproducer: https://gist.github.com/eustlb/cfcea58b4ffabfd45b4b6fce5ab283ed
+        """
+        audio = load_audio(
+            "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/fleur_es_sample.wav",
+            sampling_rate=16000,
+        )
+        inputs = self.processor(audio, sampling_rate=16000, return_tensors="pt", language="es", punctuation=True)
+        model = CohereAsrForConditionalGeneration.from_pretrained(
+            self.checkpoint_name, device_map=torch_device, revision="refs/pr/6"
+        )
+        inputs.to(model.device, dtype=model.dtype)
+
+        outputs = model.generate(**inputs, max_new_tokens=256)
+        text = self.processor.decode(outputs, skip_special_tokens=True)
+
+        EXPECTED_OUTPUT = [" Esto parece tener sentido ya que en la Tierra no se percibe su movimiento, ¿cierto?"]
+        self.assertEqual(text, EXPECTED_OUTPUT)