diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml
index 02595f30db2..016d7279353 100644
--- a/docs/source/en/_toctree.yml
+++ b/docs/source/en/_toctree.yml
@@ -740,6 +740,8 @@
title: Mimi
- local: model_doc/mms
title: MMS
+ - local: model_doc/moshi
+ title: Moshi
- local: model_doc/musicgen
title: MusicGen
- local: model_doc/musicgen_melody
diff --git a/docs/source/en/index.md b/docs/source/en/index.md
index 32a730e6bcf..bdea11a2456 100644
--- a/docs/source/en/index.md
+++ b/docs/source/en/index.md
@@ -223,6 +223,7 @@ Flax), PyTorch, and/or TensorFlow.
| [MobileNetV2](model_doc/mobilenet_v2) | ✅ | ❌ | ❌ |
| [MobileViT](model_doc/mobilevit) | ✅ | ✅ | ❌ |
| [MobileViTV2](model_doc/mobilevitv2) | ✅ | ❌ | ❌ |
+| [Moshi](model_doc/moshi) | ✅ | ❌ | ❌ |
| [MPNet](model_doc/mpnet) | ✅ | ✅ | ❌ |
| [MPT](model_doc/mpt) | ✅ | ❌ | ❌ |
| [MRA](model_doc/mra) | ✅ | ❌ | ❌ |
diff --git a/docs/source/en/model_doc/mimi.md b/docs/source/en/model_doc/mimi.md
index 486d1836334..ad15a002da9 100644
--- a/docs/source/en/model_doc/mimi.md
+++ b/docs/source/en/model_doc/mimi.md
@@ -66,4 +66,4 @@ The original code can be found [here](https://github.com/kyutai-labs/moshi).
[[autodoc]] MimiModel
- decode
- encode
- - forward
+ - forward
\ No newline at end of file
diff --git a/docs/source/en/model_doc/moshi.md b/docs/source/en/model_doc/moshi.md
new file mode 100644
index 00000000000..64216f570e3
--- /dev/null
+++ b/docs/source/en/model_doc/moshi.md
@@ -0,0 +1,183 @@
+
+
+# Moshi
+
+## Overview
+
+The Moshi model was proposed in [Moshi: a speech-text foundation model for real-time dialogue](https://kyutai.org/Moshi.pdf) by Alexandre Défossez, Laurent Mazaré, Manu Orsini, Amélie Royer, Patrick Pérez, Hervé Jégou, Edouard Grave and Neil Zeghidour.
+
+Moshi is a speech-text foundation model that casts spoken dialogue as speech-to-speech generation. Starting from a text language model backbone, Moshi generates speech as tokens from the residual quantizer of a neural audio codec, while modeling separately its own speech and that of the user into parallel streams. This allows for the removal of explicit speaker turns, and the modeling of arbitrary conversational dynamics. Moshi also predicts time-aligned text tokens as a prefix to audio tokens. This “Inner Monologue” method significantly improves the linguistic quality of generated speech and provides streaming speech recognition and text-to-speech. As a result, Moshi is the first real-time full-duplex spoken large language model, with a theoretical latency of 160ms, 200ms in practice.
+
+
+
+
+
+
",
+ unk_token="",
+ eos_token="",
+ )
+ tokenizer.pad_token = tokenizer.eos_token
+ tokenizer.save_pretrained(self.tmpdirname)
+
+ def get_rust_tokenizer(self, **kwargs) -> PreTrainedTokenizerFast:
+ return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
+
+ @unittest.skip(reason="No slow tokenizer")
+ def test_added_tokens_serialization(self):
+ pass
+
+ @unittest.skip(reason="PreTrainedTokenizerFast doesn't have tokenizer_file in its signature")
+ def test_rust_tokenizer_signature(self):
+ pass
+
+ @unittest.skip(reason="No slow tokenizer")
+ def test_encode_decode_with_spaces(self):
+ pass
+
+ def test_full_tokenizer(self):
+ tokenizer = PreTrainedTokenizerFast(
+ tokenizer_object=MoshiConverter(vocab_file=SAMPLE_VOCAB).converted(),
+ bos_token="",
+ unk_token="",
+ eos_token="",
+ )
+
+ tokens = tokenizer.tokenize("This is a test")
+ self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
+
+ self.assertListEqual(
+ tokenizer.convert_tokens_to_ids(tokens),
+ [285, 46, 10, 170, 382],
+ )
+
+ tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
+ self.assertListEqual(
+ tokens,
+ [
+ SPIECE_UNDERLINE + "I",
+ SPIECE_UNDERLINE + "was",
+ SPIECE_UNDERLINE + "b",
+ "or",
+ "n",
+ SPIECE_UNDERLINE + "in",
+ SPIECE_UNDERLINE + "",
+ "9",
+ "2",
+ "0",
+ "0",
+ "0",
+ ",",
+ SPIECE_UNDERLINE + "and",
+ SPIECE_UNDERLINE + "this",
+ SPIECE_UNDERLINE + "is",
+ SPIECE_UNDERLINE + "f",
+ "al",
+ "s",
+ "é",
+ ".",
+ ],
+ )
+ ids = tokenizer.convert_tokens_to_ids(tokens)
+ self.assertListEqual(
+ ids,
+ [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],
+ )
+
+ back_tokens = tokenizer.convert_ids_to_tokens(ids)
+ self.assertListEqual(
+ back_tokens,
+ [
+ SPIECE_UNDERLINE + "I",
+ SPIECE_UNDERLINE + "was",
+ SPIECE_UNDERLINE + "b",
+ "or",
+ "n",
+ SPIECE_UNDERLINE + "in",
+ SPIECE_UNDERLINE + "",
+ "",
+ "2",
+ "0",
+ "0",
+ "0",
+ ",",
+ SPIECE_UNDERLINE + "and",
+ SPIECE_UNDERLINE + "this",
+ SPIECE_UNDERLINE + "is",
+ SPIECE_UNDERLINE + "f",
+ "al",
+ "s",
+ "",
+ ".",
+ ],
+ )
+
+ def test_special_tokens_initialization(self):
+ for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
+ with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
+ added_tokens = [AddedToken("", lstrip=True)]
+
+ tokenizer_r = self.rust_tokenizer_class.from_pretrained(
+ pretrained_name, additional_special_tokens=added_tokens, **kwargs
+ )
+ r_output = tokenizer_r.encode("Hey this is a token")
+
+ special_token_id = tokenizer_r.encode("", add_special_tokens=False)[0]
+
+ self.assertTrue(special_token_id in r_output)
+
+ def test_picklable(self):
+ with tempfile.NamedTemporaryFile() as f:
+ shutil.copyfile(SAMPLE_VOCAB, f.name)
+ tokenizer = PreTrainedTokenizerFast(
+ tokenizer_object=MoshiConverter(vocab_file=f.name).converted(),
+ bos_token="",
+ unk_token="",
+ eos_token="",
+ )
+ pickled_tokenizer = pickle.dumps(tokenizer)
+ pickle.loads(pickled_tokenizer)
+
+ def test_training_new_tokenizer(self):
+ # This feature only exists for fast tokenizers
+ if not self.test_rust_tokenizer:
+ self.skipTest(reason="test_rust_tokenizer is set to False")
+
+ tokenizer = self.get_rust_tokenizer()
+ new_tokenizer = tokenizer.train_new_from_iterator(SMALL_TRAINING_CORPUS, 100)
+
+ # Test we can use the new tokenizer with something not seen during training
+ inputs = new_tokenizer(["This is the first sentence", "This sentence is different 🤗."])
+ self.assertEqual(len(inputs["input_ids"]), 2)
+ decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)
+ expected_result = "This is the first sentence"
+
+ self.assertEqual(expected_result, decoded_input)
+
+ # We check that the parameters of the tokenizer remained the same
+ # Check we have the same number of added_tokens for both pair and non-pair inputs.
+ self.assertEqual(tokenizer.num_special_tokens_to_add(False), new_tokenizer.num_special_tokens_to_add(False))
+ self.assertEqual(tokenizer.num_special_tokens_to_add(True), new_tokenizer.num_special_tokens_to_add(True))
+
+ # Check we have the correct max_length for both pair and non-pair inputs.
+ self.assertEqual(tokenizer.max_len_single_sentence, new_tokenizer.max_len_single_sentence)
+ self.assertEqual(tokenizer.max_len_sentences_pair, new_tokenizer.max_len_sentences_pair)
+
+ # Assert the set of special tokens match as we didn't ask to change them
+ self.assertSequenceEqual(
+ tokenizer.all_special_tokens_extended,
+ new_tokenizer.all_special_tokens_extended,
+ )
+
+ self.assertDictEqual(tokenizer.special_tokens_map, new_tokenizer.special_tokens_map)
+
+ def test_training_new_tokenizer_with_special_tokens_change(self):
+ # This feature only exists for fast tokenizers
+ if not self.test_rust_tokenizer:
+ self.skipTest(reason="test_rust_tokenizer is set to False")
+
+ tokenizer = self.get_rust_tokenizer()
+ # Test with a special tokens map
+ class_signature = inspect.signature(tokenizer.__class__)
+ if "cls_token" in class_signature.parameters:
+ new_tokenizer = tokenizer.train_new_from_iterator(
+ SMALL_TRAINING_CORPUS, 100, special_tokens_map={tokenizer.cls_token: ""}
+ )
+ cls_id = new_tokenizer.get_vocab()[""]
+ self.assertEqual(new_tokenizer.cls_token, "")
+ self.assertEqual(new_tokenizer.cls_token_id, cls_id)
+
+ # Create a new mapping from the special tokens defined in the original tokenizer
+ special_tokens_list = SpecialTokensMixin.SPECIAL_TOKENS_ATTRIBUTES.copy()
+ special_tokens_list.remove("additional_special_tokens")
+ special_tokens_map = {}
+ for token in special_tokens_list:
+ # Get the private one to avoid unnecessary warnings.
+ if getattr(tokenizer, f"_{token}") is not None:
+ special_token = getattr(tokenizer, token)
+ special_tokens_map[special_token] = f"{special_token}a"
+
+ # Train new tokenizer
+ new_tokenizer = tokenizer.train_new_from_iterator(
+ SMALL_TRAINING_CORPUS, 100, special_tokens_map=special_tokens_map
+ )
+
+ # Check the changes
+ for token in special_tokens_list:
+ # Get the private one to avoid unnecessary warnings.
+ if getattr(tokenizer, f"_{token}") is None:
+ continue
+ special_token = getattr(tokenizer, token)
+ if special_token in special_tokens_map:
+ new_special_token = getattr(new_tokenizer, token)
+ self.assertEqual(special_tokens_map[special_token], new_special_token)
+
+ new_id = new_tokenizer.get_vocab()[new_special_token]
+ self.assertEqual(getattr(new_tokenizer, f"{token}_id"), new_id)
+
+ # Check if the AddedToken / string format has been kept
+ for special_token in tokenizer.all_special_tokens_extended:
+ if isinstance(special_token, AddedToken) and special_token.content not in special_tokens_map:
+ # The special token must appear identically in the list of the new tokenizer.
+ self.assertTrue(
+ special_token in new_tokenizer.all_special_tokens_extended,
+ f"'{special_token}' should be in {new_tokenizer.all_special_tokens_extended}",
+ )
+ elif isinstance(special_token, AddedToken):
+ # The special token must appear in the list of the new tokenizer as an object of type AddedToken with
+ # the same parameters as the old AddedToken except the content that the user has requested to change.
+ special_token_str = special_token.content
+ new_special_token_str = special_tokens_map[special_token_str]
+
+ find = False
+ for candidate in new_tokenizer.all_special_tokens_extended:
+ if (
+ isinstance(candidate, AddedToken)
+ and candidate.content == new_special_token_str
+ and candidate.lstrip == special_token.lstrip
+ and candidate.rstrip == special_token.rstrip
+ and candidate.normalized == special_token.normalized
+ and candidate.single_word == special_token.single_word
+ ):
+ find = True
+ break
+ special_token.content = new_special_token_str
+ self.assertTrue(
+ find,
+ f"'{special_token.__repr__()}' should appear as an `AddedToken` in the all_special_tokens_extended = "
+ f"{[k for k in new_tokenizer.all_special_tokens_extended if str(k)==new_special_token_str]} but it is missing"
+ ", this means that the new tokenizers did not keep the `rstrip`, `lstrip`, `normalized` etc attributes.",
+ )
+ elif special_token not in special_tokens_map:
+ # The special token must appear identically in the list of the new tokenizer.
+ self.assertTrue(
+ special_token in new_tokenizer.all_special_tokens_extended,
+ f"'{special_token.__repr__()}' should be in {new_tokenizer.all_special_tokens_extended}",
+ )
+
+ else:
+ # The special token must appear in the list of the new tokenizer as an object of type string.
+ self.assertTrue(special_tokens_map[special_token] in new_tokenizer.all_special_tokens_extended)
+
+ # Test we can use the new tokenizer with something not seen during training
+ inputs = new_tokenizer(["This is the first sentence", "This sentence is different 🤗."])
+ self.assertEqual(len(inputs["input_ids"]), 2)
+ decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)
+ expected_result = "This is the first sentence"
+
+ self.assertEqual(expected_result, decoded_input)
+
+ def test_alignement_methods(self):
+ # TODO: @ArthurZucker - alignment is broken
+ pass
+
+ def test_added_tokens_do_lower_case(self):
+ # TODO: @ArthurZucker
+ pass
+
+
+@require_torch
+@require_sentencepiece
+@require_tokenizers
+class MoshiIntegrationTest(unittest.TestCase):
+ @classmethod
+ def setUpClass(cls):
+ checkpoint_name = "kmhf/hf-moshiko"
+ cls.rust_tokenizer = AutoTokenizer.from_pretrained(checkpoint_name)
+ return cls
+
+ @require_torch
+ def integration_tests(self):
+ inputs = self.tokenizer(
+ ["The following string should be properly encoded: Hello.", "But ird and ปี ird ด"],
+ return_tensors="pt",
+ )
+
+ long_attention_mask = [1] * 21
+
+ # fmt: off
+ self.assertEqual(
+ nested_simplify(inputs),
+ {
+ "input_ids": [
+ [287, 547, 2359, 457, 297, 3708, 11488, 279, 11725, 263],
+ [588, 478, 1442, 267, 260, 228, 188, 159, 228, 188, 185, 260, 260, 478, 1442, 260, 260, 260, 228, 188, 152],
+ ],
+ "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1], long_attention_mask],
+ },
+ )
+ # fmt: on
+
+ def test_fast_special_tokens(self):
+ fast_tokenizer = self.rust_tokenizer
+
+ fast_tokenizer.add_eos_token = False
+ fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)
+ assert fast == [318, 1145, 694]
+
+ fast_tokenizer.add_eos_token = True
+ fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)
+ assert fast == [318, 1145, 694]
+
+ self.rust_tokenizer.add_eos_token = False
+
+ def test_simple_encode_decode(self):
+ rust_tokenizer = self.rust_tokenizer
+
+ self.assertEqual(rust_tokenizer.encode("This is a test"), [353, 275, 272, 694])
+ self.assertEqual(rust_tokenizer.decode([353, 275, 272, 694], skip_special_tokens=True), "This is a test")
+
+ # bytefallback showcase
+ bytefallback_tokens = [260, 235, 152, 163, 234, 184, 191, 13340, 235, 160, 163, 236, 180, 159, 234, 156, 179] # fmt: skip
+ self.assertEqual(rust_tokenizer.encode("生活的真谛是"), bytefallback_tokens)
+ self.assertEqual(
+ rust_tokenizer.decode(bytefallback_tokens, skip_special_tokens=True),
+ "生活的真谛是",
+ )
+
+ # Inner spaces showcase
+ self.assertEqual(rust_tokenizer.encode("Hi Hello"), [2769, 260, 11725])
+ self.assertEqual(rust_tokenizer.decode([2769, 260, 11725], skip_special_tokens=True), "Hi Hello")
+
+ self.assertEqual(rust_tokenizer.encode("Hi Hello"), [2769, 260, 260, 11725])
+ self.assertEqual(rust_tokenizer.decode([2769, 260, 260, 11725], skip_special_tokens=True), "Hi Hello")
+
+ # TODO: @ArthurZucker
+ # self.assertEqual(rust_tokenizer.encode(""), [])
+
+ # self.assertEqual(rust_tokenizer.encode(" "), [260, 260])
+
+ # self.assertEqual(rust_tokenizer.encode(" "), [260, 260, 260])
+
+ # self.assertEqual(rust_tokenizer.encode(" Hello"), [260, 11725])
+
+ # self.assertEqual(rust_tokenizer.encode(""), [607, 266, 578])
+
+ def test_no_differences_decode(self):
+ rust_tokenizer = self.rust_tokenizer
+
+ self.assertEqual(rust_tokenizer.decode([869]), "levels")
+
+ self.assertEqual(rust_tokenizer.decode([30112, 869]), "unanswered levels")
+
+
+@require_sentencepiece
+@require_tokenizers
+class CommonSpmIntegrationTests(unittest.TestCase):
+ """
+ A class that regroups important test to make sure that we properly handle the special tokens.
+ """
+
+ def test_edge_case_tabulation(self):
+ fast_tokenizer = AutoTokenizer.from_pretrained("kmhf/hf-moshiko")
+ input_text = "Hey. \t\t \n\nyou é @#😈 🤗! , 1234 15 5,61"
+ EXPECTED_IDS = [11510, 934, 4451, 266, 578, 263, 260, 13, 13, 260, 14, 14, 5209, 260, 260, 1202, 260, 527, 1322, 244, 163, 156, 140, 260, 260, 244, 163, 168, 155, 430, 1047, 261, 260, 265, 270, 278, 281, 260, 265, 280, 260, 280, 261, 285, 265] # fmt: skip
+ EXPECTED_TOKENS = ['▁Hey', '<', 'eo', 's', '>', '.', '▁', '<0x09>', '<0x09>', '▁', '<0x0A>', '<0x0A>', 'you', '▁', '▁', 'é', '▁', '▁@', '#', '<0xF0>', '<0x9F>', '<0x98>', '<0x88>', '▁', '▁', '<0xF0>', '<0x9F>', '<0xA4>', '<0x97>', '!', '▁▁▁▁▁▁▁', ',', '▁', '1', '2', '3', '4', '▁', '1', '5', '▁', '5', ',', '6', '1'] # fmt: skip
+
+ tokens = fast_tokenizer.tokenize(input_text)
+ with self.subTest("test fast edge case fast"):
+ self.assertEqual(tokens, EXPECTED_TOKENS)
+
+ input_ids = fast_tokenizer.encode(input_text)
+ with self.subTest("test fast edge case fast"):
+ self.assertEqual(input_ids, EXPECTED_IDS)
+
+ text = fast_tokenizer.decode(EXPECTED_IDS)
+ with self.subTest("test fast edge case fast"):
+ self.assertEqual(text, "Hey. \t\t \n\nyou é @#😈 🤗! , 1234 15 5,61")
+
+ input_text = "\t\t\t\t \n\n61"
+ EXPECTED_IDS = [260, 13, 13, 13, 13, 260, 14, 14, 285, 265]
+ EXPECTED_TOKENS = ["▁", "<0x09>", "<0x09>", "<0x09>", "<0x09>", "▁", "<0x0A>", "<0x0A>", "6", "1"]
+
+ tokens = fast_tokenizer.tokenize(input_text)
+ with self.subTest("test fast edge case fast"):
+ self.assertEqual(tokens, EXPECTED_TOKENS)
+
+ input_ids = fast_tokenizer.encode(input_text)
+ with self.subTest("test fast edge case fast"):
+ self.assertEqual(input_ids, EXPECTED_IDS)
+
+ text = fast_tokenizer.decode(EXPECTED_IDS)
+ with self.subTest("test fast edge case fast"):
+ self.assertEqual(text, "\t\t\t\t \n\n61")
diff --git a/utils/check_repo.py b/utils/check_repo.py
index 98f96bcc78a..6872dada3d9 100644
--- a/utils/check_repo.py
+++ b/utils/check_repo.py
@@ -327,6 +327,7 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
"SiglipVisionModel",
"SiglipTextModel",
"ChameleonVQVAE", # no autoclass for VQ-VAE models
+ "MoshiForConditionalGeneration", # no auto class for speech-to-speech
]
# DO NOT edit this list!