Adding chunking for whisper (all seq2seq actually). Very crude matching algorithm. (#20104)

* Very crude matching algorithm.

* Fixing tests.

* Removing comments

* Adding warning + fix short matches.

* Cleanup tests.

* Quality.

* Less noisy.

* Fixup.

tests/pipelines/test_pipelines_automatic_speech_recognition.py

@@ -144,12 +144,8 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase, metaclass=Pipel
         waveform = np.tile(np.arange(1000, dtype=np.float32), 34)
         output = speech_recognizer(waveform)
         self.assertEqual(output, {"text": "(Applaudissements)"})
-        with self.assertRaises(ValueError) as v:
-            _ = speech_recognizer(waveform, chunk_length_s=10)
-        self.assertEqual(
-            str(v.exception),
-            "`chunk_length_s` is only valid for CTC models, use other chunking options for other models",
-        )
+        output = speech_recognizer(waveform, chunk_length_s=10)
+        self.assertEqual(output, {"text": "(Applaudissements)"})
 
         # Non CTC models cannot use return_timestamps
         with self.assertRaises(ValueError) as v:
@@ -261,6 +257,22 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase, metaclass=Pipel
         output = speech_recognizer(filename)
         self.assertEqual(output, {"text": "A MAN SAID TO THE UNIVERSE SIR I EXIST"})
 
+    @require_torch
+    @slow
+    def test_torch_whisper(self):
+        speech_recognizer = pipeline(
+            task="automatic-speech-recognition",
+            model="openai/whisper-tiny",
+            framework="pt",
+        )
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
+        filename = ds[40]["file"]
+        output = speech_recognizer(filename)
+        self.assertEqual(output, {"text": " A man said to the universe, Sir, I exist."})
+
+        output = speech_recognizer([filename], chunk_length_s=5, batch_size=4)
+        self.assertEqual(output, [{"text": " A man said to the universe, Sir, I exist."}])
+
     @require_torch
     @slow
     def test_torch_speech_encoder_decoder(self):