Update references from .pretrained to .inference (#2483)

asumagic · web-flow · commit 75049f430125 · 2024-04-05T15:09:05.000+02:00
diff --git a/README.md b/README.md
@@ -49,7 +49,7 @@ python train.py hparams/train.yaml
 - Each model comes with a user-friendly interface for seamless inference. For example, transcribing speech using a pretrained model requires just three lines of code:
 
 ```python
-from speechbrain.pretrained import EncoderDecoderASR
+from speechbrain.inference import EncoderDecoderASR
 
 asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-conformer-transformerlm-librispeech", savedir="pretrained_models/asr-transformer-transformerlm-librispeech")
 asr_model.transcribe_file("speechbrain/asr-conformer-transformerlm-librispeech/example.wav")
diff --git a/recipes/CommonLanguage/lang_id/README.md b/recipes/CommonLanguage/lang_id/README.md
@@ -26,7 +26,7 @@ Basically, you can run inference with only few lines of code:
 
 ```python
 import torchaudio
-from speechbrain.pretrained import EncoderClassifier
+from speechbrain.inference import EncoderClassifier
 classifier = EncoderClassifier.from_hparams(source="speechbrain/lang-id-commonlanguage_ecapa", savedir="pretrained_models/lang-id-commonlanguage_ecapa")
 
 # Italian Example
diff --git a/recipes/LibriParty/VAD/README.md b/recipes/LibriParty/VAD/README.md
@@ -29,7 +29,7 @@ The pre-trained model + easy inference is available on HuggingFace:
 Basically, you can run inference with only a few lines of code:
 
 ```python
-from speechbrain.pretrained import VAD
+from speechbrain.inference import VAD
 
 VAD = VAD.from_hparams(source="speechbrain/vad-crdnn-libriparty", savedir="pretrained_models/vad-crdnn-libriparty")
 boundaries = VAD.get_speech_segments("speechbrain/vad-crdnn-libriparty/example_vad.wav")
diff --git a/recipes/VoxLingua107/lang_id/README.md b/recipes/VoxLingua107/lang_id/README.md
@@ -93,7 +93,7 @@ You can run inference with only few lines of code:
 
 ```python
 import torchaudio
-from speechbrain.pretrained import EncoderClassifier
+from speechbrain.inference import EncoderClassifier
 language_id = EncoderClassifier.from_hparams(source="speechbrain/lang-id-voxlingua107-ecapa", savedir="tmp")
 # Download Thai language sample from Omniglot and convert to suitable form
 signal = language_id.load_audio("https://omniglot.com/soundfiles/udhr/udhr_th.mp3")
diff --git a/speechbrain/inference/interfaces.py b/speechbrain/inference/interfaces.py
@@ -69,7 +69,7 @@ def foreign_class(
     ---------
     source : str or Path or FetchSource
         The location to use for finding the model. See
-        ``speechbrain.pretrained.fetching.fetch`` for details.
+        ``speechbrain.utils.fetching.fetch`` for details.
     hparams_file : str
         The name of the hyperparameters file to use for constructing
         the modules necessary for inference. Must contain two keys:
@@ -412,7 +412,7 @@ def from_hparams(
         ---------
         source : str
             The location to use for finding the model. See
-            ``speechbrain.pretrained.fetching.fetch`` for details.
+            ``speechbrain.utils.fetching.fetch`` for details.
         hparams_file : str
             The name of the hyperparameters file to use for constructing
             the modules necessary for inference. Must contain two keys:
diff --git a/tests/utils/README.md b/tests/utils/README.md
@@ -29,7 +29,7 @@ Depending on the testing need, `test.yaml` grows - some examples
 1. [ssl-wav2vec2-base-librispeech/test.yaml](https://github.com/speechbrain/speechbrain/blob/hf-interface-testing/updates_pretrained_models/ssl-wav2vec2-base-librispeech/test.yaml) - the play between test sample, interface class, and batch function is handled via HF testing in `tests/utils`
    ```yaml
    sample: example.wav # test audio provided via HF repo
-   cls: WaveformEncoder # existing speechbrain.pretrained.interfaces class
+   cls: WaveformEncoder # existing speechbrain.inference class
    fnx: encode_batch # it's batch-wise function after audio loading
    ```
 2. [asr-wav2vec2-librispeech/test.yaml](https://github.com/speechbrain/speechbrain/blob/hf-interface-testing/updates_pretrained_models/asr-wav2vec2-librispeech/test.yaml) - testing single example & against a dataset test partition
