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support instruct s2s
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@ -413,6 +413,8 @@ class AsrDataModule:
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ultrachat_cuts = load_manifest_lazy(
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self.args.manifest_dir / "cuts_ultrachat_train.jsonl.gz"
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)
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VoiceAssistant_cuts = VoiceAssistant_cuts.resample(16000)
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ultrachat_cuts = ultrachat_cuts.resample(16000)
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return CutSet.mux(
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VoiceAssistant_cuts,
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ultrachat_cuts,
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@ -427,6 +429,7 @@ class AsrDataModule:
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VoiceAssistant_cuts = load_manifest_lazy(
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self.args.manifest_dir / "cuts_voice_assistant.00000.jsonl.gz"
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)
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VoiceAssistant_cuts = VoiceAssistant_cuts.resample(16000)
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return VoiceAssistant_cuts
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@lru_cache()
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@ -435,6 +438,7 @@ class AsrDataModule:
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VoiceAssistant_cuts = load_manifest_lazy(
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self.args.manifest_dir / "cuts_voice_assistant_small.00000.jsonl.gz"
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)
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VoiceAssistant_cuts = VoiceAssistant_cuts.resample(16000)
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return {"test": VoiceAssistant_cuts}
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@lru_cache()
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@ -482,36 +486,36 @@ class AsrDataModule:
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librispeech_clean_100_cuts = CutSet.from_huggingface_dataset(
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librispeech_clean_100,
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audio_key=self.args.audio_key,
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text_key=self.args.text_key,
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audio_key="audio",
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text_key="text",
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)
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librispeech_other_cuts = CutSet.from_huggingface_dataset(
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librispeech_other,
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audio_key=self.args.audio_key,
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text_key=self.args.text_key,
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audio_key="audio",
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text_key="text",
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)
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librispeech_clean_360_cuts = CutSet.from_huggingface_dataset(
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librispeech_clean_360,
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audio_key=self.args.audio_key,
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text_key=self.args.text_key,
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audio_key="audio",
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text_key="text",
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)
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gigaspeech_cuts = CutSet.from_huggingface_dataset(
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gigaspeech, audio_key=self.args.audio_key, text_key=self.args.text_key
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gigaspeech, audio_key="audio", text_key="text"
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)
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people_speech_clean_cuts = CutSet.from_huggingface_dataset(
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people_speech_clean,
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audio_key=self.args.audio_key,
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text_key=self.args.text_key,
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audio_key="audio",
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text_key="text",
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)
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people_speech_dirty_sa_cuts = CutSet.from_huggingface_dataset(
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people_speech_dirty_sa,
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audio_key=self.args.audio_key,
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text_key=self.args.text_key,
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audio_key="audio",
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text_key="text",
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)
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return CutSet.mux(
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@ -540,8 +544,8 @@ class AsrDataModule:
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)
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librispeech_clean_valid_cuts = CutSet.from_huggingface_dataset(
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librispeech_clean_valid,
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audio_key=self.args.audio_key,
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text_key=self.args.text_key,
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audio_key="audio",
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text_key="text",
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)
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return librispeech_clean_valid_cuts
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@ -567,20 +571,20 @@ class AsrDataModule:
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librispeech_clean_100_cuts = CutSet.from_huggingface_dataset(
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librispeech_clean_100,
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audio_key=self.args.audio_key,
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text_key=self.args.text_key,
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audio_key="audio",
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text_key="text",
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)
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librispeech_other_cuts = CutSet.from_huggingface_dataset(
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librispeech_other,
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audio_key=self.args.audio_key,
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text_key=self.args.text_key,
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audio_key="audio",
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text_key="text",
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)
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librispeech_clean_360_cuts = CutSet.from_huggingface_dataset(
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librispeech_clean_360,
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audio_key=self.args.audio_key,
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text_key=self.args.text_key,
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audio_key="audio",
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text_key="text",
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)
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return CutSet.mux(
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@ -603,7 +607,148 @@ class AsrDataModule:
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)
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gigaspeech_cuts = CutSet.from_huggingface_dataset(
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gigaspeech, audio_key=self.args.audio_key, text_key=self.args.text_key
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gigaspeech, audio_key="audio", text_key="text"
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)
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return gigaspeech_cuts
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@lru_cache()
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def train_cuts_instruct_s2s(self) -> CutSet:
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logging.info("About to get train cuts")
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if self.args.huggingface_dataset_path_or_name is not None:
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data_path = self.args.huggingface_dataset_path_or_name + "/InstructS2S-200K"
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else:
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data_path = "yuekai/InstructS2S-200K"
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# 148_688
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instruct_s2s_train = load_dataset(
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data_path, split="train", streaming=True
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)
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instruct_s2s_train_cuts = CutSet.from_huggingface_dataset(
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instruct_s2s_train,
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audio_key="question_audio",
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text_key="answer",
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)
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instruct_s2s_train_cuts = instruct_s2s_train_cuts.resample(16000)
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return instruct_s2s_train_cuts
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@lru_cache()
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def train_cuts_en_speech2speech(self) -> CutSet:
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logging.info("About to get train cuts")
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VoiceAssistant_cuts = load_manifest_lazy(
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self.args.manifest_dir / "cuts_voice_assistant_00001-00049.jsonl.gz"
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)
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ultrachat_cuts = load_manifest_lazy(
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self.args.manifest_dir / "cuts_ultrachat_train.jsonl.gz"
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)
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if self.args.huggingface_dataset_path_or_name is not None:
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data_path = self.args.huggingface_dataset_path_or_name + "/InstructS2S-200K"
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else:
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data_path = "yuekai/InstructS2S-200K"
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# 148_688
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instruct_s2s_train = load_dataset(
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data_path, split="train", streaming=True
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)
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instruct_s2s_train_cuts = CutSet.from_huggingface_dataset(
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instruct_s2s_train,
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audio_key="question_audio",
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text_key="answer",
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)
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instruct_s2s_train_cuts = instruct_s2s_train_cuts.resample(16000)
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return CutSet.mux(
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VoiceAssistant_cuts,
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ultrachat_cuts,
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instruct_s2s_train_cuts,
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weights=[
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len(VoiceAssistant_cuts),
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len(ultrachat_cuts),
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423_000,
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],
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)
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@lru_cache()
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def train_cuts_en_speech2speech_librispeech(self) -> CutSet:
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logging.info("About to get train cuts")
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VoiceAssistant_cuts = load_manifest_lazy(
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self.args.manifest_dir / "cuts_voice_assistant_00001-00049.jsonl.gz"
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)
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ultrachat_cuts = load_manifest_lazy(
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self.args.manifest_dir / "cuts_ultrachat_train.jsonl.gz"
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)
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if self.args.huggingface_dataset_path_or_name is not None:
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data_path = self.args.huggingface_dataset_path_or_name + "/InstructS2S-200K"
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else:
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data_path = "yuekai/InstructS2S-200K"
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# 148_688
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instruct_s2s_train = load_dataset(
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data_path, split="train", streaming=True
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)
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instruct_s2s_train_cuts = CutSet.from_huggingface_dataset(
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instruct_s2s_train,
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audio_key="question_audio",
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text_key="answer",
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)
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instruct_s2s_train_cuts = instruct_s2s_train_cuts.resample(16000)
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if self.args.huggingface_dataset_path_or_name is not None:
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librispeech_path = self.args.huggingface_dataset_path_or_name + "/librispeech_asr"
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else:
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librispeech_path = "fixie-ai/librispeech_asr"
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# 148_688
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librispeech_other = load_dataset(
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librispeech_path, "other", split="train.500", streaming=True
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)
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# 104_014
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librispeech_clean_360 = load_dataset(
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librispeech_path, "clean", split="train.360", streaming=True
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)
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# 28_539
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librispeech_clean_100 = load_dataset(
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librispeech_path, "clean", split="train.100", streaming=True
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)
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librispeech_clean_100_cuts = CutSet.from_huggingface_dataset(
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librispeech_clean_100,
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audio_key="audio",
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text_key="text",
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)
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librispeech_other_cuts = CutSet.from_huggingface_dataset(
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librispeech_other,
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audio_key="audio",
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text_key="text",
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)
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librispeech_clean_360_cuts = CutSet.from_huggingface_dataset(
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librispeech_clean_360,
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audio_key="audio",
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text_key="text",
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)
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return CutSet.mux(
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librispeech_other_cuts,
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VoiceAssistant_cuts,
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ultrachat_cuts,
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librispeech_clean_360_cuts,
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instruct_s2s_train_cuts,
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librispeech_clean_100_cuts,
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weights=[
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148688,
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len(VoiceAssistant_cuts),
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len(ultrachat_cuts),
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104014,
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423_000,
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28539,
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],
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)
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@ -193,6 +193,13 @@ def get_parser():
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""",
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)
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parser.add_argument(
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"--last-stage-model-path",
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type=str,
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default=None,
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help="""The path to the last stage model if it is not None. Training will start from this model.
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""",
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)
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parser.add_argument(
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"--sampler-state-dict-path",
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type=str,
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@ -229,13 +236,6 @@ def get_parser():
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help="Whether to unfreeze speech adaptor during training.",
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)
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parser.add_argument(
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"--prompt-template",
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type=str,
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default="speech_qa",
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help="The prompt template to use.",
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)
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parser.add_argument(
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"--dataset",
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type=str,
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@ -300,7 +300,6 @@ def get_params() -> AttributeDict:
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def extract_text_and_speech_token(
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batch: dict,
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prompt_template: str,
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enable_speech_output: bool
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) -> Tuple[List[Dict[str, str]], Optional[List[Any]]]:
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"""
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@ -325,54 +324,54 @@ def extract_text_and_speech_token(
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answers = batch["supervisions"]["text"]
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batch_size = len(answers)
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if prompt_template == "speech_qa":
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for i in range(batch_size):
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message_list_item = []
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if 'round' in batch["supervisions"]["cut"][i].custom:
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# slam_omni format dataset
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current_question_with_history = batch["supervisions"]["cut"][i].custom["question"]
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total_round = batch["supervisions"]["cut"][i].custom["round"]
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history_context = current_question_with_history.rsplit("<USER>:", 1)[0].strip()
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if total_round > 1:
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history_question_answer = history_context.split("USER:")
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history_question_answer = [item for item in history_question_answer if item]
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for j in range(total_round - 1):
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question_answer = history_question_answer[j].split("ASSISTANT:")
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message_list_item += [
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{"role": "user", "content": question_answer[0].strip()},
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{"role": "assistant", "content": question_answer[1].strip()},
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]
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message_list_item += [
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{"role": "user", "content": f"{DEFAULT_SPEECH_TOKEN}"},
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{"role": "assistant", "content": answers[i]},
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]
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messages.append(message_list_item)
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prompt_template_dict = {
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"speech_qa": f"{DEFAULT_SPEECH_TOKEN}",
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"speech_continuation": f"Continue the following text using less than 50 words:\\n\\n{DEFAULT_SPEECH_TOKEN}",
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"asr": f"Transcribe the following audio into text:\\n\\n{DEFAULT_SPEECH_TOKEN}",
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}
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elif prompt_template == "speech_continuation":
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# speech_tokens remains None
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for i in range(batch_size):
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message_list_item = [
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{
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"role": "user",
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"content": f"Continue the following text using less than 50 words:\\n\\n{DEFAULT_SPEECH_TOKEN}",
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},
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{"role": "assistant", "content": answers[i]},
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]
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messages.append(message_list_item)
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for i in range(batch_size):
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# Initialize prompt_template with the original default.
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# The 'prompt_template' argument to the function seems unused if we determine it here.
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# For now, I will proceed assuming the internal logic dictates the template.
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# If the function argument `prompt_template` was meant to be the default, this logic would need adjustment.
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current_prompt_template = "speech_qa" # Default value for prompt_template for the current item
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target = answers[i]
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message_list_item = []
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custom_data = batch["supervisions"]["cut"][i].custom
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elif prompt_template == "asr":
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# speech_tokens remains None
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for i in range(batch_size):
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message_list_item = [
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{
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"role": "user",
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"content": f"Transcribe the following audio into text:\\n\\n{DEFAULT_SPEECH_TOKEN}",
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},
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{"role": "assistant", "content": answers[i]},
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]
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messages.append(message_list_item)
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else:
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raise ValueError(f"Unknown prompt template: {prompt_template}")
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if 'round' in custom_data:
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# slam_omni format dataset
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# For 'round' type, the current interaction's user prompt will use current_prompt_template ("speech_qa")
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current_question_with_history = custom_data["question"]
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total_round = custom_data["round"]
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history_context = current_question_with_history.rsplit("<USER>:", 1)[0].strip()
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if total_round > 1:
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history_question_answer = history_context.split("USER:")
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history_question_answer = [item for item in history_question_answer if item]
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for j in range(total_round - 1):
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question_answer = history_question_answer[j].split("ASSISTANT:")
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message_list_item += [
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{"role": "user", "content": question_answer[0].strip()},
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{"role": "assistant", "content": question_answer[1].strip()},
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]
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elif 'continuation' in custom_data:
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# see https://huggingface.co/datasets/fixie-ai/librispeech_asr
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ASR_PROBABILITY = 0.3
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if random.random() < ASR_PROBABILITY:
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current_prompt_template = "asr"
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else:
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current_prompt_template = "speech_continuation"
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target = custom_data["continuation"]
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else:
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# single-round, speech2speech conversation data
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pass
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message_list_item += [
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{"role": "user", "content": prompt_template_dict[current_prompt_template]},
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{"role": "assistant", "content": target},
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]
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messages.append(message_list_item)
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return messages, speech_tokens
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@ -428,14 +427,17 @@ def preprocess(
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def process_batch_text_continuation(batch: dict):
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messages = []
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for i in range(len(batch["supervisions"]["text"])):
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transcript = batch["supervisions"]["cut"][i].custom["text"]
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transcripts = batch["supervisions"]["text"]
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continuations = [
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cut.custom["continuation"] for cut in batch["supervisions"]["cut"]
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]
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for i in range(len(transcripts)):
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message = [
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{
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"role": "user",
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"content": f"Continue the following text using less than 50 words:\n\n{transcript}{DEFAULT_SPEECH_TOKEN}",
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"content": f"Continue the following text using less than 50 words:\n\n{transcripts[i]}{DEFAULT_SPEECH_TOKEN}",
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},
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{"role": "assistant", "content": batch["supervisions"]["text"][i]},
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{"role": "assistant", "content": continuations[i]},
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]
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messages.append(message)
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return messages
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@ -532,7 +534,7 @@ def compute_loss(
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# WAR: TODO FIXME merge process_batch_slam_omni and process_batch_vocalnet
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messages, answer_cosyvoice_speech_token = extract_text_and_speech_token(
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batch, params.prompt_template, params.enable_speech_output
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batch, params.enable_speech_output
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)
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input_ids, attention_mask, target_ids = preprocess(messages, tokenizer)
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@ -550,7 +552,6 @@ def compute_loss(
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labels=target_ids.to(device),
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)
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elif params.loss_type == "kl_div":
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assert params.prompt_template == "speech_continuation"
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messages_text = process_batch_text_continuation(batch)
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(
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teacher_input_ids,
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@ -942,15 +943,18 @@ def run(rank, world_size, args):
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teacher_llm=teacher_llm,
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)
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||||
if params.pretrained_model_path:
|
||||
checkpoint = torch.load(params.pretrained_model_path, map_location="cpu")
|
||||
missing_keys, unexpected_keys = model.load_state_dict(checkpoint, strict=False)
|
||||
# set params.batch_idx_train according to the checkpoint name
|
||||
if "checkpoint-" in params.pretrained_model_path:
|
||||
params.batch_idx_train = int(
|
||||
params.pretrained_model_path.split("-")[-1].split("/")[0]
|
||||
)
|
||||
|
||||
if params.pretrained_model_path or params.last_stage_model_path:
|
||||
if params.pretrained_model_path is None:
|
||||
checkpoint = torch.load(params.last_stage_model_path, map_location="cpu")
|
||||
missing_keys, unexpected_keys = model.load_state_dict(checkpoint, strict=False)
|
||||
else:
|
||||
checkpoint = torch.load(params.pretrained_model_path, map_location="cpu")
|
||||
missing_keys, unexpected_keys = model.load_state_dict(checkpoint, strict=False)
|
||||
# set params.batch_idx_train according to the checkpoint name
|
||||
if "checkpoint-" in params.pretrained_model_path:
|
||||
params.batch_idx_train = int(
|
||||
params.pretrained_model_path.split("-")[-1].split("/")[0]
|
||||
)
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
@ -999,6 +1003,12 @@ def run(rank, world_size, args):
|
||||
f"Exclude cut with ID {c.id} from training. Duration: {c.duration}, lenth: {codec_len}"
|
||||
)
|
||||
return False
|
||||
if "question" in c.custom:
|
||||
if len(c.custom["question"]) > 1200:
|
||||
# logging.warning(
|
||||
# f"Exclude cut with ID {c.id} from training. question length: {len(c.custom['question'])}"
|
||||
# )
|
||||
return False
|
||||
return True
|
||||
|
||||
if params.dataset == "slam_omni_belle":
|
||||
@ -1007,6 +1017,12 @@ def run(rank, world_size, args):
|
||||
elif params.dataset == "vocalnet_ultrachat_voiceassistant":
|
||||
train_cuts = data_module.train_cuts_en_vocalnet()
|
||||
valid_cuts = data_module.valid_cuts_en_vocalnet()
|
||||
elif params.dataset == "vocalnet_ultrachat_voiceassistant_instruct_s2s":
|
||||
train_cuts = data_module.train_cuts_en_speech2speech()
|
||||
valid_cuts = data_module.valid_cuts_en_vocalnet()
|
||||
elif params.dataset == "vocalnet_ultrachat_voiceassistant_instruct_s2s_librispeech":
|
||||
train_cuts = data_module.train_cuts_en_speech2speech_librispeech()
|
||||
valid_cuts = data_module.valid_cuts_en_vocalnet()
|
||||
elif params.dataset == "ultravox_multi_en":
|
||||
train_cuts = data_module.train_cuts_ultravox()
|
||||
valid_cuts = data_module.valid_cuts_ultravox()
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user