fix

.github/workflows/model_jobs.yml

@@ -97,6 +97,14 @@ jobs:
         run: |
           python3 utils/print_env.py
 
+      - name: Install datasets main
+        working-directory: /transformers
+        run: python3 -m pip install --no-cache-dir git+https://github.com/huggingface/datasets.git@main
+
+      - name: Install torchcodec
+        working-directory: /transformers
+        run: python3 -m pip install --no-cache-dir torch torchvision torchaudio torchcodec --index-url https://download.pytorch.org/whl/cu126
+
       - name: Show installed libraries and their versions
         working-directory: /transformers
         run: pip freeze

.github/workflows/self-scheduled-caller.yml

@@ -7,7 +7,7 @@ on:
     - cron: "17 2 * * *"
   push:
     branches:
-      - run_scheduled_ci*
+      - fix-dataset-run_object_detection-and-add-torchcodec-trigger-ci
   workflow_dispatch:
     inputs:
       prev_workflow_run_id:
@@ -24,7 +24,7 @@ on:
 
 # Used for `push` to easily modify the target workflow runs to compare against
 env:
-    prev_workflow_run_id: ""
+    prev_workflow_run_id: "15988665799"
     other_workflow_run_id: ""
 
 
@@ -50,64 +50,8 @@ jobs:
     uses: ./.github/workflows/self-scheduled.yml
     with:
       job: run_models_gpu
-      slack_report_channel: "#transformers-ci-daily-models"
+      slack_report_channel: "#transformers-dummy"
       docker: huggingface/transformers-all-latest-gpu
       ci_event: Daily CI
       report_repo_id: hf-internal-testing/transformers_daily_ci
     secrets: inherit
-
-  torch-pipeline:
-    name: Torch pipeline CI
-    uses: ./.github/workflows/self-scheduled.yml
-    with:
-      job: run_pipelines_torch_gpu
-      slack_report_channel: "#transformers-ci-daily-pipeline-torch"
-      docker: huggingface/transformers-pytorch-gpu
-      ci_event: Daily CI
-      report_repo_id: hf-internal-testing/transformers_daily_ci
-    secrets: inherit
-
-  example-ci:
-    name: Example CI
-    uses: ./.github/workflows/self-scheduled.yml
-    with:
-      job: run_examples_gpu
-      slack_report_channel: "#transformers-ci-daily-examples"
-      docker: huggingface/transformers-all-latest-gpu
-      ci_event: Daily CI
-      report_repo_id: hf-internal-testing/transformers_daily_ci
-    secrets: inherit
-
-  trainer-fsdp-ci:
-    name: Trainer/FSDP CI
-    uses: ./.github/workflows/self-scheduled.yml
-    with:
-      job: run_trainer_and_fsdp_gpu
-      slack_report_channel: "#transformers-ci-daily-training"
-      docker: huggingface/transformers-all-latest-gpu
-      ci_event: Daily CI
-      report_repo_id: hf-internal-testing/transformers_daily_ci
-    secrets: inherit
-
-  deepspeed-ci:
-    name: DeepSpeed CI
-    uses: ./.github/workflows/self-scheduled.yml
-    with:
-      job: run_torch_cuda_extensions_gpu
-      slack_report_channel: "#transformers-ci-daily-training"
-      docker: huggingface/transformers-pytorch-deepspeed-latest-gpu
-      ci_event: Daily CI
-      working-directory-prefix: /workspace
-      report_repo_id: hf-internal-testing/transformers_daily_ci
-    secrets: inherit
-
-  quantization-ci:
-    name: Quantization CI
-    uses: ./.github/workflows/self-scheduled.yml
-    with:
-      job: run_quantization_torch_gpu
-      slack_report_channel: "#transformers-ci-daily-quantization"
-      docker: huggingface/transformers-quantization-latest-gpu
-      ci_event: Daily CI
-      report_repo_id: hf-internal-testing/transformers_daily_ci
-    secrets: inherit

docker/examples-torch.dockerfile

@@ -2,10 +2,10 @@ FROM python:3.9-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
-RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git
+RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git ffmpeg
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
+RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' 'torchcodec' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing]" seqeval albumentations jiwer
 RUN uv pip uninstall transformers

docker/pipeline-torch.dockerfile

@@ -2,10 +2,10 @@ FROM python:3.9-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
-RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git pkg-config openssh-client git
+RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git pkg-config openssh-client git ffmpeg
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
+RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' 'torchcodec' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing]"
 RUN uv pip uninstall transformers

docker/torch-light.dockerfile

@@ -2,10 +2,10 @@ FROM python:3.9-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
-RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs
+RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs ffmpeg
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
+RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' 'torchcodec' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing,tiktoken,num2words,video]"
 RUN uv pip uninstall transformers

docs/source/en/model_doc/speech_to_text_2.md

@@ -64,15 +64,15 @@ predicted token ids.
 >>> import torch
 >>> from transformers import Speech2Text2Processor, SpeechEncoderDecoderModel
 >>> from datasets import load_dataset
->>> import soundfile as sf
+>>> from torchcodec.decoders import AudioDecoder
 
 >>> model = SpeechEncoderDecoderModel.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
 >>> processor = Speech2Text2Processor.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
 
 
 >>> def map_to_array(batch):
-...     speech, _ = sf.read(batch["file"])
+...     decoder = AudioDecoder(batch["file"])
-...     batch["speech"] = speech
+...     batch["speech"] = decoder.get_all_samples().data
 ...     return batch
 
 

examples/pytorch/_tests_requirements.txt

@@ -22,6 +22,7 @@ protobuf
 torch
 torchvision
 torchaudio
+torchcodec
 jiwer
 librosa
 evaluate >= 0.2.0

examples/pytorch/object-detection/requirements.txt

@@ -1,5 +1,5 @@
 albumentations >= 1.4.16
 timm
-datasets
+datasets>=4.0
 torchmetrics
 pycocotools

examples/pytorch/object-detection/run_object_detection.py

@@ -399,7 +399,7 @@ def main():
         dataset["validation"] = split["test"]
 
     # Get dataset categories and prepare mappings for label_name <-> label_id
-    categories = dataset["train"].features["objects"].feature["category"].names
+    categories = dataset["train"].features["objects"]["category"].feature.names
     id2label = dict(enumerate(categories))
     label2id = {v: k for k, v in id2label.items()}
 

examples/pytorch/object-detection/run_object_detection_no_trainer.py

@@ -460,7 +460,7 @@ def main():
         dataset["validation"] = split["test"]
 
     # Get dataset categories and prepare mappings for label_name <-> label_id
-    categories = dataset["train"].features["objects"].feature["category"].names
+    categories = dataset["train"].features["objects"]["category"].feature.names
     id2label = dict(enumerate(categories))
     label2id = {v: k for k, v in id2label.items()}
 

src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py

@@ -436,9 +436,10 @@ class ASTModel(ASTPreTrainedModel):
         input_values (`torch.FloatTensor` of shape `(batch_size, max_length, num_mel_bins)`):
             Float values mel features extracted from the raw audio waveform. Raw audio waveform can be obtained by
             loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via
-            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
+            the torchcodec library (`pip install torchcodec`) or the soundfile library (`pip install soundfile`).
-            [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
+            To prepare the array into `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the
-            tensor of type `torch.FloatTensor`. See [`~ASTFeatureExtractor.__call__`]
+            mel features, padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`~ASTFeatureExtractor.__call__`]
         """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -526,9 +527,10 @@ class ASTForAudioClassification(ASTPreTrainedModel):
         input_values (`torch.FloatTensor` of shape `(batch_size, max_length, num_mel_bins)`):
             Float values mel features extracted from the raw audio waveform. Raw audio waveform can be obtained by
             loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via
-            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
+            the torchcodec library (`pip install torchcodec`) or the soundfile library (`pip install soundfile`).
-            [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
+            To prepare the array into `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the
-            tensor of type `torch.FloatTensor`. See [`~ASTFeatureExtractor.__call__`]
+            mel features, padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`~ASTFeatureExtractor.__call__`]
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the audio classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If

src/transformers/models/data2vec/modeling_data2vec_audio.py

@@ -1053,9 +1053,10 @@ class Data2VecAudioForSequenceClassification(Data2VecAudioPreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`Data2VecAudioProcessor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`Data2VecAudioProcessor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
@@ -1167,9 +1168,10 @@ class Data2VecAudioForAudioFrameClassification(Data2VecAudioPreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`Data2VecAudioProcessor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`Data2VecAudioProcessor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
@@ -1349,9 +1351,10 @@ class Data2VecAudioForXVector(Data2VecAudioPreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`Data2VecAudioProcessor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`Data2VecAudioProcessor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If

src/transformers/models/hubert/modeling_hubert.py

@@ -985,15 +985,15 @@ class HubertModel(HubertPreTrainedModel):
         ```python
         >>> from transformers import AutoProcessor, HubertModel
         >>> from datasets import load_dataset
-        >>> import soundfile as sf
+        >>> from torchcodec.decoders import AudioDecoder
 
         >>> processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
         >>> model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft")
 
 
         >>> def map_to_array(batch):
-        ...     speech, _ = sf.read(batch["file"])
+        ...     decoder = AudioDecoder(batch["file"])
-        ...     batch["speech"] = speech
+        ...     batch["speech"] = decoder.get_all_samples().data
         ...     return batch
 
 
@@ -1261,9 +1261,10 @@ class HubertForSequenceClassification(HubertPreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`HubertProcessor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`HubertProcessor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If

src/transformers/models/hubert/modeling_tf_hubert.py

@@ -1459,15 +1459,15 @@ class TFHubertModel(TFHubertPreTrainedModel):
         ```python
         >>> from transformers import AutoProcessor, TFHubertModel
         >>> from datasets import load_dataset
-        >>> import soundfile as sf
+        >>> from torchcodec.decoders import AudioDecoder
 
         >>> processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
         >>> model = TFHubertModel.from_pretrained("facebook/hubert-large-ls960-ft")
 
 
         >>> def map_to_array(batch):
-        ...     speech, _ = sf.read(batch["file"])
+        ...     decoder = AudioDecoder(batch["file"])
-        ...     batch["speech"] = speech
+        ...     batch["speech"] = decoder.get_all_samples().data
         ...     return batch
 
 
@@ -1571,15 +1571,15 @@ class TFHubertForCTC(TFHubertPreTrainedModel):
         >>> import tensorflow as tf
         >>> from transformers import AutoProcessor, TFHubertForCTC
         >>> from datasets import load_dataset
-        >>> import soundfile as sf
+        >>> from torchcodec.decoders import AudioDecoder
 
         >>> processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
         >>> model = TFHubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft")
 
 
         >>> def map_to_array(batch):
-        ...     speech, _ = sf.read(batch["file"])
+        ...     decoder = AudioDecoder(batch["file"])
-        ...     batch["speech"] = speech
+        ...     batch["speech"] = decoder.get_all_samples().data
         ...     return batch
 
 

src/transformers/models/hubert/modular_hubert.py

@@ -239,15 +239,15 @@ class HubertModel(Wav2Vec2Model, HubertPreTrainedModel):
         ```python
         >>> from transformers import AutoProcessor, HubertModel
         >>> from datasets import load_dataset
-        >>> import soundfile as sf
+        >>> from torchcodec.decoders import AudioDecoder
 
         >>> processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
         >>> model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft")
 
 
         >>> def map_to_array(batch):
-        ...     speech, _ = sf.read(batch["file"])
+        ...     decoder = AudioDecoder(batch["file"])
-        ...     batch["speech"] = speech
+        ...     batch["speech"] = decoder.get_all_samples().data
         ...     return batch
 
 

src/transformers/models/moonshine/modeling_moonshine.py

@@ -563,7 +563,8 @@ class MoonshineEncoder(MoonshinePreTrainedModel):
             input_values (`torch.FloatTensor` of shape `(batch_size, audio_length)`):
                 Float values of the raw speech waveform. Raw speech waveform can be
                 obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
-                `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
+                `numpy.ndarray`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+                the soundfile library (`pip install soundfile`). To prepare the array into
                 `input_values`, the [`AutoFeatureExtractor`] should be used for padding
                 and conversion into a tensor of type `torch.FloatTensor`.
             attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
@@ -1028,7 +1029,8 @@ class MoonshineModel(MoonshinePreTrainedModel):
         input_values (`torch.FloatTensor` of shape `(batch_size, audio_length)`):
             Float values of the raw speech waveform. Raw speech waveform can be
             obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
-            `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
+            `numpy.ndarray`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+            the soundfile library (`pip install soundfile`). To prepare the array into
             `input_values`, the [`AutoFeatureExtractor`] should be used for padding
             and conversion into a tensor of type `torch.FloatTensor`.
         decoder_input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
@@ -1204,7 +1206,8 @@ class MoonshineForConditionalGeneration(MoonshinePreTrainedModel, GenerationMixi
         input_values (`torch.FloatTensor` of shape `(batch_size, audio_length)`):
             Float values of the raw speech waveform. Raw speech waveform can be
             obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
-            `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
+            `numpy.ndarray`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+            the soundfile library (`pip install soundfile`). To prepare the array into
             `input_values`, the [`AutoFeatureExtractor`] should be used for padding
             and conversion into a tensor of type `torch.FloatTensor`.
         decoder_input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):

src/transformers/models/moonshine/modular_moonshine.py

@@ -587,7 +587,8 @@ class MoonshineEncoder(MoonshinePreTrainedModel):
             input_values (`torch.FloatTensor` of shape `(batch_size, audio_length)`):
                 Float values of the raw speech waveform. Raw speech waveform can be
                 obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
-                `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
+                `numpy.ndarray`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+                the soundfile library (`pip install soundfile`). To prepare the array into
                 `input_values`, the [`AutoFeatureExtractor`] should be used for padding
                 and conversion into a tensor of type `torch.FloatTensor`.
             attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
@@ -844,7 +845,8 @@ class MoonshineModel(WhisperModel):
         input_values (`torch.FloatTensor` of shape `(batch_size, audio_length)`):
             Float values of the raw speech waveform. Raw speech waveform can be
             obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
-            `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
+            `numpy.ndarray`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+            the soundfile library (`pip install soundfile`). To prepare the array into
             `input_values`, the [`AutoFeatureExtractor`] should be used for padding
             and conversion into a tensor of type `torch.FloatTensor`.
         decoder_input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
@@ -1004,7 +1006,8 @@ class MoonshineForConditionalGeneration(MoonshinePreTrainedModel, GenerationMixi
         input_values (`torch.FloatTensor` of shape `(batch_size, audio_length)`):
             Float values of the raw speech waveform. Raw speech waveform can be
             obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
-            `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
+            `numpy.ndarray`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+            the soundfile library (`pip install soundfile`). To prepare the array into
             `input_values`, the [`AutoFeatureExtractor`] should be used for padding
             and conversion into a tensor of type `torch.FloatTensor`.
         decoder_input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):

src/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py

@@ -797,7 +797,8 @@ class Qwen2_5OmniAudioEncoder(Qwen2_5OmniPreTrainedModel):
         input_features (`torch.LongTensor` of shape `(batch_size, feature_size, sequence_length)`):
             Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be
             obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
-            `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
+            `numpy.ndarray`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+            the soundfile library (`pip install soundfile`). To prepare the array into
             `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding
             and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
         feature_lens (`torch.LongTensor` of shape `(batch_size,)`):
@@ -1789,9 +1790,10 @@ class Qwen2_5OmniThinkerForConditionalGeneration(Qwen2_5OmniPreTrainedModelForCo
         input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, feature_sequence_length)`):
             Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
             loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via
-            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
+            the torchcodec library (`pip install torchcodec`) or the soundfile library (`pip install soundfile`).
-            [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
+            To prepare the array into `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the
-            tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
+            mel features, padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`~WhisperFeatureExtractor.__call__`]
         pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size), *optional*):
             The tensors corresponding to the input videos. Pixel values can be obtained using
             [`AutoImageProcessor`]. See [`SiglipImageProcessor.__call__`] for details ([]`NewTaskModelProcessor`] uses

src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py

@@ -1782,7 +1782,8 @@ class Qwen2_5OmniAudioEncoder(Qwen2_5OmniPreTrainedModel):
         input_features (`torch.LongTensor` of shape `(batch_size, feature_size, sequence_length)`):
             Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be
             obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
-            `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
+            `numpy.ndarray`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+            the soundfile library (`pip install soundfile`). To prepare the array into
             `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding
             and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
         feature_lens (`torch.LongTensor` of shape `(batch_size,)`):
@@ -2236,9 +2237,10 @@ class Qwen2_5OmniThinkerForConditionalGeneration(Qwen2_5OmniPreTrainedModelForCo
         input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, feature_sequence_length)`):
             Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
             loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via
-            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
+            the torchcodec library (`pip install torchcodec`) or the soundfile library (`pip install soundfile`).
-            [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
+            To prepare the array into `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the
-            tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
+            mel features, padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`~WhisperFeatureExtractor.__call__`]
         pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size), *optional*):
             The tensors corresponding to the input videos. Pixel values can be obtained using
             [`AutoImageProcessor`]. See [`SiglipImageProcessor.__call__`] for details ([]`NewTaskModelProcessor`] uses

src/transformers/models/qwen2_audio/modeling_qwen2_audio.py

@@ -360,7 +360,8 @@ class Qwen2AudioEncoder(Qwen2AudioPreTrainedModel):
             input_features (`torch.LongTensor` of shape `(batch_size, feature_size, sequence_length)`):
                 Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be
                 obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
-                `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
+                `numpy.ndarray`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+                the soundfile library (`pip install soundfile`). To prepare the array into
                 `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding
                 and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
             attention_mask (`torch.Tensor`)`, *optional*):
@@ -740,9 +741,10 @@ class Qwen2AudioForConditionalGeneration(Qwen2AudioPreTrainedModel, GenerationMi
         input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, feature_sequence_length)`):
             Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
             loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via
-            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
+            the torchcodec library (`pip install torchcodec`) or the soundfile library (`pip install soundfile`).
-            [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
+            To prepare the array into `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the
-            tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
+            mel features, padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`~WhisperFeatureExtractor.__call__`]
         feature_attention_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`):
             Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`:
 

src/transformers/models/sew/modeling_sew.py

@@ -1077,9 +1077,10 @@ class SEWForSequenceClassification(SEWPreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`SEWProcessor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`SEWProcessor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If

src/transformers/models/sew_d/modeling_sew_d.py

@@ -1597,9 +1597,10 @@ class SEWDForSequenceClassification(SEWDPreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`SEWDProcessor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`SEWDProcessor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If

src/transformers/models/speech_encoder_decoder/modeling_flax_speech_encoder_decoder.py

@@ -86,8 +86,9 @@ SPEECH_ENCODER_DECODER_INPUTS_DOCSTRING = r"""
     Args:
         inputs (`jnp.ndarray` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, feature_dim)`, *optional*):
             Float values of input raw speech waveform or speech features. Values can be obtained by loading a `.flac`
-            or `.wav` audio file into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile
+            or `.wav` audio file into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip
-            library (`pip install soundfile`). To prepare the array into `inputs`, either the [`Wav2Vec2Processor`] or
+            install torchcodec`) or the soundfile library (`pip install soundfile`).
+            To prepare the array into `inputs`, either the [`Wav2Vec2Processor`] or
             [`Speech2TextProcessor`] should be used for padding and conversion into a tensor of type
             `torch.FloatTensor`.
         attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):

src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py

@@ -339,8 +339,9 @@ class SpeechEncoderDecoderModel(PreTrainedModel, GenerationMixin):
         r"""
         inputs (`torch.FloatTensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, feature_dim)`, *optional*):
             Float values of input raw speech waveform or speech features. Values can be obtained by loading a `.flac`
-            or `.wav` audio file into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile
+            or `.wav` audio file into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip
-            library (`pip install soundfile`). To prepare the array into `inputs`, either the [`Wav2Vec2Processor`] or
+            install torchcodec`) or the soundfile library (`pip install soundfile`).
+            To prepare the array into `inputs`, either the [`Wav2Vec2Processor`] or
             [`Speech2TextProcessor`] should be used for padding and conversion into a tensor of type
             `torch.FloatTensor`.
         decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):

src/transformers/models/speech_to_text/modeling_speech_to_text.py

@@ -620,7 +620,8 @@ class Speech2TextEncoder(Speech2TextPreTrainedModel):
             input_features (`torch.LongTensor` of shape `(batch_size, sequence_length, feature_size)`):
                 Float values of fbank features extracted from the raw speech waveform. Raw speech waveform can be
                 obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
-                `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
+                `numpy.ndarray`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+            the soundfile library (`pip install soundfile`). To prepare the array into
                 `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the fbank features,
                 padding and conversion into a tensor of type `torch.FloatTensor`. See
                 [`~Speech2TextFeatureExtractor.__call__`]

src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py

@@ -848,7 +848,8 @@ class TFSpeech2TextEncoder(keras.layers.Layer):
             input_features (`tf.Tensor` of shape `(batch_size, sequence_length, feature_size)`):
                 Float values of fbank features extracted from the raw speech waveform. Raw speech waveform can be
                 obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
-                `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
+                `numpy.ndarray`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+            the soundfile library (`pip install soundfile`). To prepare the array into
                 `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the fbank features,
                 padding and conversion into a tensor of floats. See [`~Speech2TextFeatureExtractor.__call__`]
             attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
@@ -1469,7 +1470,7 @@ class TFSpeech2TextForConditionalGeneration(TFSpeech2TextPreTrainedModel, TFCaus
         >>> import tensorflow as tf
         >>> from transformers import Speech2TextProcessor, TFSpeech2TextForConditionalGeneration
         >>> from datasets import load_dataset
-        >>> import soundfile as sf
+        >>> from torchcodec.decoders import AudioDecoder
 
         >>> model = TFSpeech2TextForConditionalGeneration.from_pretrained(
         ...     "facebook/s2t-small-librispeech-asr", from_pt=True
@@ -1478,8 +1479,8 @@ class TFSpeech2TextForConditionalGeneration(TFSpeech2TextPreTrainedModel, TFCaus
 
 
         >>> def map_to_array(batch):
-        ...     speech, _ = sf.read(batch["file"])
+        ...     decoder = AudioDecoder(batch["file"])
-        ...     batch["speech"] = speech
+        ...     batch["speech"] = decoder.get_all_samples().data
         ...     return batch
 
 

src/transformers/models/unispeech/modeling_unispeech.py

@@ -1486,9 +1486,10 @@ class UniSpeechForSequenceClassification(UniSpeechPreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`UniSpeechProcessor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`UniSpeechProcessor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If

src/transformers/models/unispeech_sat/modeling_unispeech_sat.py

@@ -1481,9 +1481,10 @@ class UniSpeechSatForSequenceClassification(UniSpeechSatPreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`UniSpeechSatProcessor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`UniSpeechSatProcessor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
@@ -1595,9 +1596,10 @@ class UniSpeechSatForAudioFrameClassification(UniSpeechSatPreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`UniSpeechSatProcessor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`UniSpeechSatProcessor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
@@ -1777,9 +1779,10 @@ class UniSpeechSatForXVector(UniSpeechSatPreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`UniSpeechSatProcessor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`UniSpeechSatProcessor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If

src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py

@@ -1064,15 +1064,15 @@ FLAX_WAV2VEC2_MODEL_DOCSTRING = """
     ```python
     >>> from transformers import AutoProcessor, FlaxWav2Vec2Model
     >>> from datasets import load_dataset
-    >>> import soundfile as sf
+    >>> from torchcodec.decoders import AudioDecoder
 
     >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-large-lv60")
     >>> model = FlaxWav2Vec2Model.from_pretrained("facebook/wav2vec2-large-lv60")
 
 
     >>> def map_to_array(batch):
-    ...     speech, _ = sf.read(batch["file"])
+    ...     decoder = AudioDecoder(batch["file"])
-    ...     batch["speech"] = speech
+    ...     batch["speech"] = decoder.get_all_samples().data
     ...     return batch
 
 
@@ -1183,15 +1183,15 @@ FLAX_WAV2VEC2_FOR_CTC_DOCSTRING = """
     >>> import jax.numpy as jnp
     >>> from transformers import AutoProcessor, FlaxWav2Vec2ForCTC
     >>> from datasets import load_dataset
-    >>> import soundfile as sf
+        >>> from torchcodec.decoders import AudioDecoder
 
     >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-large-960h-lv60")
     >>> model = FlaxWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60")
 
 
     >>> def map_to_array(batch):
-    ...     speech, _ = sf.read(batch["file"])
+    ...     decoder = AudioDecoder(batch["file"])
-    ...     batch["speech"] = speech
+    ...     batch["speech"] = decoder.get_all_samples().data
     ...     return batch
 
 
@@ -1384,15 +1384,15 @@ FLAX_WAV2VEC2_FOR_PRETRAINING_DOCSTRING = """
     >>> from transformers import AutoFeatureExtractor, FlaxWav2Vec2ForPreTraining
     >>> from transformers.models.wav2vec2.modeling_flax_wav2vec2 import _compute_mask_indices
     >>> from datasets import load_dataset
-    >>> import soundfile as sf
+    >>> from torchcodec.decoders import AudioDecoder
 
     >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-large-lv60")
     >>> model = FlaxWav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-large-lv60")
 
 
     >>> def map_to_array(batch):
-    ...     speech, _ = sf.read(batch["file"])
+    ...     decoder = AudioDecoder(batch["file"])
-    ...     batch["speech"] = speech
+    ...     batch["speech"] = decoder.get_all_samples().data
     ...     return batch
 
 

src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py

@@ -1530,15 +1530,15 @@ class TFWav2Vec2Model(TFWav2Vec2PreTrainedModel):
         ```python
         >>> from transformers import AutoProcessor, TFWav2Vec2Model
         >>> from datasets import load_dataset
-        >>> import soundfile as sf
+        >>> from torchcodec.decoders import AudioDecoder
 
         >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
         >>> model = TFWav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
 
 
         >>> def map_to_array(batch):
-        ...     speech, _ = sf.read(batch["file"])
+        ...     decoder = AudioDecoder(batch["file"])
-        ...     batch["speech"] = speech
+        ...     batch["speech"] = decoder.get_all_samples().data
         ...     return batch
 
 
@@ -1642,15 +1642,15 @@ class TFWav2Vec2ForCTC(TFWav2Vec2PreTrainedModel):
         >>> import tensorflow as tf
         >>> from transformers import AutoProcessor, TFWav2Vec2ForCTC
         >>> from datasets import load_dataset
-        >>> import soundfile as sf
+        >>> from torchcodec.decoders import AudioDecoder
 
         >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
         >>> model = TFWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
 
 
         >>> def map_to_array(batch):
-        ...     speech, _ = sf.read(batch["file"])
+        ...     decoder = AudioDecoder(batch["file"])
-        ...     batch["speech"] = speech
+        ...     batch["speech"] = decoder.get_all_samples().data
         ...     return batch
 
 

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -2012,9 +2012,10 @@ class Wav2Vec2ForSequenceClassification(Wav2Vec2PreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`Wav2Vec2Processor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`Wav2Vec2Processor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
@@ -2126,9 +2127,10 @@ class Wav2Vec2ForAudioFrameClassification(Wav2Vec2PreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`Wav2Vec2Processor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`Wav2Vec2Processor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
@@ -2308,9 +2310,10 @@ class Wav2Vec2ForXVector(Wav2Vec2PreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`Wav2Vec2Processor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`Wav2Vec2Processor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If

src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py

@@ -1579,9 +1579,10 @@ class Wav2Vec2ConformerForSequenceClassification(Wav2Vec2ConformerPreTrainedMode
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`Wav2Vec2ConformerProcessor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`Wav2Vec2ConformerProcessor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
@@ -1681,9 +1682,10 @@ class Wav2Vec2ConformerForAudioFrameClassification(Wav2Vec2ConformerPreTrainedMo
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`Wav2Vec2ConformerProcessor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`Wav2Vec2ConformerProcessor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
@@ -1851,9 +1853,10 @@ class Wav2Vec2ConformerForXVector(Wav2Vec2ConformerPreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`Wav2Vec2ConformerProcessor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`Wav2Vec2ConformerProcessor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If

src/transformers/models/wavlm/modeling_wavlm.py

@@ -1328,9 +1328,10 @@ class WavLMForSequenceClassification(WavLMPreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`WavLMProcessor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`WavLMProcessor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
@@ -1442,9 +1443,10 @@ class WavLMForAudioFrameClassification(WavLMPreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`WavLMProcessor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`WavLMProcessor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
@@ -1624,9 +1626,10 @@ class WavLMForXVector(WavLMPreTrainedModel):
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
             Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+            into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-            soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+            torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-            conversion into a tensor of type `torch.FloatTensor`. See [`WavLMProcessor.__call__`] for details.
+            the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`WavLMProcessor.__call__`] for details.
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If

src/transformers/models/whisper/modeling_flax_whisper.py

@@ -102,9 +102,10 @@ WHISPER_INPUTS_DOCSTRING = r"""
         input_features (`numpy.ndarray` of shape `(batch_size, feature_size, sequence_length)`):
             Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
             loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via
-            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
+            the torchcodec library (`pip install torchcodec`) or the soundfile library (`pip install soundfile`).
-            [`WhisperFeatureExtractor`] should be used for extracting the features, padding and conversion into a
+            To prepare the array into `input_features`, the [`WhisperFeatureExtractor`] should be used for extracting
-            tensor of type `numpy.ndarray`. See [`~WhisperFeatureExtractor.__call__`]
+            the features, padding and conversion into a tensor of type `numpy.ndarray`.
+            See [`~WhisperFeatureExtractor.__call__`]
         attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
             Whisper does not support masking of the `input_features`, this argument is preserved for compatibility, but
             is not used. By default the silence in the input log mel spectrogram are ignored.
@@ -139,9 +140,10 @@ WHISPER_ENCODE_INPUTS_DOCSTRING = r"""
         input_features (`numpy.ndarray` of shape `(batch_size, feature_size, sequence_length)`):
             Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
             loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via
-            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
+            the torchcodec library (`pip install torchcodec`) or the soundfile library (`pip install soundfile`).
-            [`WhisperFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
+            To prepare the array into `input_features`, the [`WhisperFeatureExtractor`] should be used for extracting
-            tensor of type `numpy.ndarray`. See [`~WhisperFeatureExtractor.__call__`].
+            the mel features, padding and conversion into a tensor of type `numpy.ndarray`.
+            See [`~WhisperFeatureExtractor.__call__`].
         attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
             Whisper does not support masking of the `input_features`, this argument is preserved for compatibility, but
             is not used. By default the silence in the input log mel spectrogram are ignored.

src/transformers/models/whisper/modeling_tf_whisper.py

@@ -601,9 +601,10 @@ WHISPER_INPUTS_DOCSTRING = r"""
         input_features (`tf.Tensor` of shape `(batch_size, feature_size, sequence_length)`):
             Float values of fbank features extracted from the raw speech waveform. Raw speech waveform can be obtained
             by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a `numpy.ndarray`, *e.g.*
-            via the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
+            via the torchcodec library (`pip install torchcodec`) or the soundfile library (`pip install soundfile`).
-            [`AutoFeatureExtractor`] should be used for extracting the fbank features, padding and conversion into a
+            To prepare the array into `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the
-            tensor of type `tf.Tensor`. See [`~WhisperFeatureExtractor.__call__`]
+            fbank features, padding and conversion into a tensor of type `tf.Tensor`.
+            See [`~WhisperFeatureExtractor.__call__`]
         decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
             Indices of decoder input sequence tokens in the vocabulary.
 
@@ -729,7 +730,8 @@ class TFWhisperEncoder(keras.layers.Layer):
             input_features (`tf.Tensor` of shape `(batch_size, feature_size, sequence_length)`):
                 Float values of fbank features extracted from the raw speech waveform. Raw speech waveform can be
                 obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
-                `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
+                `numpy.ndarray`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+                the soundfile library (`pip install soundfile`). To prepare the array into
                 `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the fbank features,
                 padding and conversion into a tensor of type `tf.Tensor`. See [`~WhisperFeatureExtractor.__call__`]
             head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):

src/transformers/models/whisper/modeling_whisper.py

@@ -651,7 +651,8 @@ class WhisperEncoder(WhisperPreTrainedModel):
             input_features (`torch.LongTensor` of shape `(batch_size, feature_size, sequence_length)`):
                 Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be
                 obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
-                `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
+                `numpy.ndarray`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+            the soundfile library (`pip install soundfile`). To prepare the array into
                 `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding
                 and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
             attention_mask (`torch.Tensor`)`, *optional*):
@@ -1096,9 +1097,10 @@ class WhisperModel(WhisperPreTrainedModel):
         input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, sequence_length)`):
             Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
             loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via
-            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
+            the torchcodec library (`pip install torchcodec`) or the soundfile library (`pip install soundfile`).
-            [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
+            To prepare the array into `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the
-            tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
+            mel features, padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`~WhisperFeatureExtractor.__call__`]
         decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
             Indices of decoder input sequence tokens in the vocabulary.
 
@@ -1266,9 +1268,10 @@ class WhisperForConditionalGeneration(WhisperGenerationMixin, WhisperPreTrainedM
         input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, sequence_length)`):
             Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
             loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via
-            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
+            the torchcodec library (`pip install torchcodec`) or the soundfile library (`pip install soundfile`).
-            [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
+            To prepare the array into `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the
-            tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
+            mel features, padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`~WhisperFeatureExtractor.__call__`]
         decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
             Indices of decoder input sequence tokens in the vocabulary.
 
@@ -1600,9 +1603,10 @@ class WhisperForAudioClassification(WhisperPreTrainedModel):
         input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, sequence_length)`):
             Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
             loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via
-            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
+            the torchcodec library (`pip install torchcodec`) or the soundfile library (`pip install soundfile`).
-            [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
+            To prepare the array into `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the
-            tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
+            mel features, padding and conversion into a tensor of type `torch.FloatTensor`.
+            See [`~WhisperFeatureExtractor.__call__`]
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If

src/transformers/testing_utils.py

@@ -130,7 +130,6 @@ from .utils import (
     is_scipy_available,
     is_sentencepiece_available,
     is_seqio_available,
-    is_soundfile_available,
     is_spacy_available,
     is_speech_available,
     is_spqr_available,
@@ -656,7 +655,7 @@ def require_torchcodec(test_case):
     These tests are skipped when Torchcodec isn't installed.
 
     """
-    return unittest.skipUnless(is_torchcodec_available(), "test requires Torchvision")(test_case)
+    return unittest.skipUnless(is_torchcodec_available(), "test requires Torchcodec")(test_case)
 
 
 def require_torch_or_tf(test_case):
@@ -1268,16 +1267,6 @@ def require_clearml(test_case):
     return unittest.skipUnless(is_clearml_available(), "test requires clearml")(test_case)
 
 
-def require_soundfile(test_case):
-    """
-    Decorator marking a test that requires soundfile
-
-    These tests are skipped when soundfile isn't installed.
-
-    """
-    return unittest.skipUnless(is_soundfile_available(), "test requires soundfile")(test_case)
-
-
 def require_deepspeed(test_case):
     """
     Decorator marking a test that requires deepspeed

src/transformers/utils/args_doc.py

@@ -248,9 +248,10 @@ class ModelArgs:
     input_values = {
         "description": """
     Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
-    into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
+    into an array of type `list[float]` or a `numpy.ndarray`, *e.g.* via the torchcodec library (`pip install
-    soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
+    torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`,
-    conversion into a tensor of type `torch.FloatTensor`. See [`{processor_class}.__call__`] for details.
+    the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`.
+    See [`{processor_class}.__call__`] for details.
     """,
         "shape": "of shape `(batch_size, sequence_length)`",
     }

tests/models/data2vec/test_modeling_data2vec_audio.py

@@ -21,7 +21,7 @@ from datasets import load_dataset
 
 from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
 from transformers import Data2VecAudioConfig, is_torch_available
-from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device
+from transformers.testing_utils import require_torch, require_torchcodec, slow, torch_device
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, _config_zero_init
@@ -656,7 +656,7 @@ class Data2VecAudioUtilsTest(unittest.TestCase):
 
 
 @require_torch
-@require_soundfile
+@require_torchcodec
 @slow
 class Data2VecAudioModelIntegrationTest(unittest.TestCase):
     def _load_datasamples(self, num_samples):

tests/models/hubert/test_modeling_hubert.py

@@ -22,7 +22,7 @@ import unittest
 import pytest
 
 from transformers import HubertConfig, is_torch_available
-from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device
+from transformers.testing_utils import require_torch, require_torchcodec, slow, torch_device
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import (
@@ -750,7 +750,7 @@ class HubertUtilsTest(unittest.TestCase):
 
 
 @require_torch
-@require_soundfile
+@require_torchcodec
 @slow
 class HubertModelIntegrationTest(unittest.TestCase):
     def _load_datasamples(self, num_samples):

tests/models/phi4_multimodal/test_modeling_phi4_multimodal.py

@@ -33,13 +33,13 @@ from transformers import (
 from transformers.testing_utils import (
     Expectations,
     cleanup,
-    require_soundfile,
     require_torch,
     require_torch_large_accelerator,
+    require_torchcodec,
     slow,
     torch_device,
 )
-from transformers.utils import is_soundfile_available
+from transformers.utils import is_torchcodec_available
 
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
@@ -54,8 +54,8 @@ if is_vision_available():
     from PIL import Image
 
 
-if is_soundfile_available():
+if is_torchcodec_available():
-    import soundfile
+    import torchcodec
 
 
 class Phi4MultimodalModelTester:
@@ -300,7 +300,8 @@ class Phi4MultimodalIntegrationTest(unittest.TestCase):
             tmp.write(requests.get(self.audio_url, stream=True).raw.data)
             tmp.flush()
             tmp.seek(0)
-            self.audio, self.sampling_rate = soundfile.read(tmp.name)
+            samples = torchcodec.decoders.AudioDecoder(tmp.name).get_all_samples()
+            self.audio, self.sampling_rate = samples.data, samples.sample_rate
 
         cleanup(torch_device, gc_collect=True)
 
@@ -378,7 +379,7 @@ class Phi4MultimodalIntegrationTest(unittest.TestCase):
 
         self.assertEqual(response, EXPECTED_RESPONSE)
 
-    @require_soundfile
+    @require_torchcodec
     def test_audio_text_generation(self):
         model = AutoModelForCausalLM.from_pretrained(
             self.checkpoint_path, revision=self.revision, torch_dtype=torch.float16, device_map=torch_device

tests/models/sew/test_modeling_sew.py

@@ -19,7 +19,7 @@ import unittest
 import pytest
 
 from transformers import SEWConfig, is_torch_available
-from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device
+from transformers.testing_utils import require_torch, require_torchcodec, slow, torch_device
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import (
@@ -453,7 +453,7 @@ class SEWUtilsTest(unittest.TestCase):
 
 
 @require_torch
-@require_soundfile
+@require_torchcodec
 @slow
 class SEWModelIntegrationTest(unittest.TestCase):
     def _load_datasamples(self, num_samples):

tests/models/sew_d/test_modeling_sew_d.py

@@ -19,7 +19,7 @@ import unittest
 import pytest
 
 from transformers import SEWDConfig, is_torch_available
-from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device
+from transformers.testing_utils import require_torch, require_torchcodec, slow, torch_device
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import (
@@ -464,7 +464,7 @@ class SEWDUtilsTest(unittest.TestCase):
 
 
 @require_torch
-@require_soundfile
+@require_torchcodec
 @slow
 class SEWDModelIntegrationTest(unittest.TestCase):
     def _load_datasamples(self, num_samples):

tests/models/unispeech/test_modeling_unispeech.py

@@ -21,7 +21,7 @@ import pytest
 from datasets import load_dataset
 
 from transformers import UniSpeechConfig, is_torch_available
-from transformers.testing_utils import is_flaky, require_soundfile, require_torch, slow, torch_device
+from transformers.testing_utils import is_flaky, require_torch, require_torchcodec, slow, torch_device
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import (
@@ -553,7 +553,7 @@ class UniSpeechRobustModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.T
 
 
 @require_torch
-@require_soundfile
+@require_torchcodec
 @slow
 class UniSpeechModelIntegrationTest(unittest.TestCase):
     def _load_datasamples(self, num_samples):

tests/models/unispeech_sat/test_modeling_unispeech_sat.py

@@ -21,7 +21,7 @@ import pytest
 from datasets import load_dataset
 
 from transformers import UniSpeechSatConfig, is_torch_available
-from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device
+from transformers.testing_utils import require_torch, require_torchcodec, slow, torch_device
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import (
@@ -807,7 +807,7 @@ class UniSpeechSatRobustModelTest(ModelTesterMixin, unittest.TestCase):
 
 
 @require_torch
-@require_soundfile
+@require_torchcodec
 @slow
 class UniSpeechSatModelIntegrationTest(unittest.TestCase):
     def _load_datasamples(self, num_samples):

tests/models/wav2vec2/test_modeling_wav2vec2.py

@@ -34,10 +34,10 @@ from transformers.testing_utils import (
     is_torchaudio_available,
     require_flash_attn,
     require_pyctcdecode,
-    require_soundfile,
     require_torch,
     require_torch_gpu,
     require_torchaudio,
+    require_torchcodec,
     run_test_in_subprocess,
     slow,
     torch_device,
@@ -1444,7 +1444,7 @@ class Wav2Vec2UtilsTest(unittest.TestCase):
 
 
 @require_torch
-@require_soundfile
+@require_torchcodec
 @slow
 class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
     def tearDown(self):