Add decoder_kwargs to send to LM on asr pipeline. (#15646)

Co-authored-by: Giuseppe Attanasio <giuseppeattanasio6@gmail.com>

Co-authored-by: Giuseppe Attanasio <giuseppeattanasio6@gmail.com>

src/transformers/pipelines/automatic_speech_recognition.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from collections import defaultdict
-from typing import TYPE_CHECKING, Union
+from typing import TYPE_CHECKING, Dict, Optional, Union
 
 import numpy as np
 
@@ -180,7 +180,11 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
         if "stride_length_s" in kwargs:
             preprocess_params["stride_length_s"] = kwargs["stride_length_s"]
 
-        return preprocess_params, {}, {}
+        postprocess_params = {}
+        if "decoder_kwargs" in kwargs:
+            postprocess_params["decoder_kwargs"] = kwargs["decoder_kwargs"]
+
+        return preprocess_params, {}, postprocess_params
 
     def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None):
         if isinstance(inputs, str):
@@ -319,7 +323,7 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
         extra = model_inputs
         return {"is_last": is_last, **out, **extra}
 
-    def postprocess(self, model_outputs):
+    def postprocess(self, model_outputs, decoder_kwargs: Optional[Dict] = None):
         if self.type == "ctc_with_lm":
             final_logits = []
             for outputs in model_outputs:
@@ -334,9 +338,11 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
                     right_n = total_n - right
                     logits = logits[:, left:right_n]
                 final_logits.append(logits)
+            if decoder_kwargs is None:
+                decoder_kwargs = {}
             logits = np.concatenate(final_logits, axis=1)
             logits = logits.squeeze(0)
-            text = self.decoder.decode_beams(logits)[0][0]
+            text = self.decoder.decode_beams(logits, **decoder_kwargs)[0][0]
         else:
             skip_special_tokens = self.type != "ctc"
             tokens = np.concatenate([outputs["tokens"].numpy() for outputs in model_outputs], axis=-1)

tests/test_pipelines_automatic_speech_recognition.py

@@ -365,10 +365,17 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase, metaclass=Pipel
         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")
 
+        # Making sure the argument are passed to the decoder
+        # Since no change happens in the result, check the error comes from
+        # the `decode_beams` function.
+        with self.assertRaises(TypeError) as e:
+            output = speech_recognizer([audio_tiled], decoder_kwargs={"num_beams": 2})
+            self.assertContains(e.msg, "TypeError: decode_beams() got an unexpected keyword argument 'num_beams'")
+        output = speech_recognizer([audio_tiled], decoder_kwargs={"beam_width": 2})
+
     @require_torch
     @require_pyctcdecode
     def test_with_local_lm_fast(self):