[ASR pipeline] correct with lm pipeline (#15200)

* [ASR pipeline] correct with lm pipeline

* improve error

tests/test_feature_extraction_auto.py

@@ -13,6 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import json
 import os
 import tempfile
 import unittest
@@ -42,8 +43,9 @@ class AutoFeatureExtractorTest(unittest.TestCase):
 
             # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
             config_dict = AutoFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR).to_dict()
+
             config_dict.pop("feature_extractor_type")
-            config = Wav2Vec2FeatureExtractor(config_dict)
+            config = Wav2Vec2FeatureExtractor(**config_dict)
 
             # save in new folder
             model_config.save_pretrained(tmpdirname)
@@ -51,6 +53,10 @@ class AutoFeatureExtractorTest(unittest.TestCase):
 
             config = AutoFeatureExtractor.from_pretrained(tmpdirname)
 
+            # make sure private variable is not incorrectly saved
+            dict_as_saved = json.loads(config.to_json_string())
+            self.assertTrue("_processor_class" not in dict_as_saved)
+
         self.assertIsInstance(config, Wav2Vec2FeatureExtractor)
 
     def test_feature_extractor_from_local_file(self):

tests/test_pipelines_automatic_speech_recognition.py

@@ -295,6 +295,26 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase, metaclass=Pipel
         self.assertEqual(output, [{"text": ANY(str)}])
         self.assertEqual(output[0]["text"][:6], "ZBT ZC")
 
+    @require_torch
+    @require_pyctcdecode
+    def test_with_lm_fast(self):
+        speech_recognizer = pipeline(
+            task="automatic-speech-recognition",
+            model="hf-internal-testing/processor_with_lm",
+            framework="pt",
+        )
+        self.assertEqual(speech_recognizer.type, "ctc_with_lm")
+
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
+        audio = ds[40]["audio"]["array"]
+
+        n_repeats = 2
+        audio_tiled = np.tile(audio, n_repeats)
+        output = speech_recognizer([audio_tiled], batch_size=2)
+
+        self.assertEqual(output, [{"text": ANY(str)}])
+        self.assertEqual(output[0]["text"][:6], "<s> <s")
+
     @require_torch
     @slow
     def test_chunking(self):