python/FunASR-XL.git

			@@ -23,7 +23,7 @@
			@tables.register("model_classes", "LLMASR")
			class LLMASR(nn.Module):
			""" """


			def __init__(
			self,
			specaug: str = None,
			@@ -59,28 +59,29 @@
			# postencoder: Optional[AbsPostEncoder] = None,
			**kwargs,
			):


			super().__init__()


			if specaug is not None:
			specaug_class = tables.specaug_classes.get(specaug)
			specaug = specaug_class(**specaug_conf)
			if normalize is not None:
			normalize_class = tables.normalize_classes.get(normalize)
			normalize = normalize_class(**normalize_conf)


			# audio encoder
			hub = audio_encoder_conf.get("hub", None)
			if hub == "ms":
			from funasr import AutoModel
			model = AutoModel(model=audio_encoder, model_revision="v2.0.4")

			model = AutoModel(model=audio_encoder, model_revision="master")
			# frontend = model.kwargs.get("frontend")
			audio_encoder_output_size = model.model.encoder_output_size

			audio_encoder = model.model.model.encoder


			# self.frontend = frontend


			elif hub == "hf":
			pass
			else:
			@@ -92,7 +93,7 @@
			for name, param in audio_encoder.named_parameters():
			param.requires_grad = False
			audio_encoder.eval()


			self.audio_encoder = audio_encoder

			# llm
			@@ -102,7 +103,7 @@
			from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig

			init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5")


			model = AutoModelForCausalLM.from_pretrained(
			init_param_path,
			load_in_8bit=None,
			@@ -115,15 +116,14 @@
			param.requires_grad = False
			model.eval()
			self.llm = model


			# adaptor
			adaptor_class = tables.adaptor_classes.get(audio_adaptor)
			audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size
			audio_adaptor = adaptor_class(**audio_adaptor_conf)


			self.audio_adaptor = audio_adaptor



			self.blank_id = blank_id
			self.sos = sos if sos is not None else vocab_size - 1
			self.eos = eos if eos is not None else vocab_size - 1
			@@ -143,7 +143,7 @@

			self.length_normalized_loss = length_normalized_loss
			self.beam_search = None


			def forward(
			self,
			speech: torch.Tensor,
			@@ -151,7 +151,7 @@
			text: torch.Tensor,
			text_lengths: torch.Tensor,
			input_ids: torch.Tensor,
			attention_mask:torch.Tensor,
			attention_mask: torch.Tensor,
			labels_ids: torch.Tensor,
			label_mask: torch.Tensor,
			audio_mask: torch.Tensor,
			@@ -170,15 +170,15 @@
			text_lengths = text_lengths[:, 0]
			if len(speech_lengths.size()) > 1:
			speech_lengths = speech_lengths[:, 0]


			batch_size = speech.shape[0]


			# audio encoder
			encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)


			# audio_adaptor
			encoder_out = self.audio_adaptor(encoder_out)


			input_ids[input_ids == -1] = 0
			input_ids[input_ids == -100] = 0
			if hasattr(self.llm.model, "embed_tokens"):
			@@ -193,11 +193,14 @@
			_, l, _ = encoder_out.shape
			# [audio, bos, prompt, input, pad]
			encoder_outs_pad = F.pad(encoder_out, (0, 0, 0, token_num - l, 0, 0), value=0.0)
			inputs_embeds = encoder_outs_pad * audio_mask[:, :, None] + inputs_embeds * (1.0-audio_mask[:, :, None])
			inputs_embeds = encoder_outs_pad * audio_mask[:, :, None] + inputs_embeds * (
			1.0 - audio_mask[:, :, None]
			)

			model_outputs = self.llm(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids)
			model_outputs = self.llm(
			inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
			)
			loss = model_outputs.loss


			stats = {}
			with torch.no_grad():
			@@ -214,34 +217,38 @@
			return loss, stats, weight

			def encode(
			self, speech: torch.Tensor, speech_lengths: torch.Tensor, **kwargs,
			self,
			speech: torch.Tensor,
			speech_lengths: torch.Tensor,
			**kwargs,
			):
			speech = speech.permute(0, 2, 1)
			res = self.audio_encoder(speech)
			if len(res) > 1:
			if isinstance(res, (list, tuple)):
			encoder_out, encoder_out_lens = res[0], res[1]
			else:
			encoder_out, encoder_out_lens = res, speech_lengths
			return encoder_out, encoder_out_lens

			def inference(self,
			data_in,
			data_lengths=None,
			key: list = None,
			tokenizer=None,
			frontend=None,
			**kwargs,
			):


			def inference(
			self,
			data_in,
			data_lengths=None,
			key: list = None,
			tokenizer=None,
			frontend=None,
			**kwargs,
			):

			prompt = kwargs.get("prompt", "Transcribe speech to text.")


			if kwargs.get("batch_size", 1) > 1:
			raise NotImplementedError("batch decoding is not implemented")



			meta_data = {}
			if isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank": # fbank
			if (
			isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
			): # fbank
			speech, speech_lengths = data_in, data_lengths
			if len(speech.shape) < 3:
			speech = speech[None, :, :]
			@@ -250,32 +257,37 @@
			else:
			# extract fbank feats
			time1 = time.perf_counter()
			audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000),
			data_type=kwargs.get("data_type", "sound"),
			tokenizer=tokenizer)
			audio_sample_list = load_audio_text_image_video(
			data_in,
			fs=frontend.fs,
			audio_fs=kwargs.get("fs", 16000),
			data_type=kwargs.get("data_type", "sound"),
			tokenizer=tokenizer,
			)
			time2 = time.perf_counter()
			meta_data["load_data"] = f"{time2 - time1:0.3f}"
			speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
			frontend=frontend)
			speech, speech_lengths = extract_fbank(
			audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend
			)
			time3 = time.perf_counter()
			meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
			meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000

			meta_data["batch_data_time"] = (
			speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
			)

			speech = speech.to(device=kwargs["device"])
			speech_lengths = speech_lengths.to(device=kwargs["device"])


			# Encoder
			encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)

			# adaptor
			encoder_out = self.audio_adaptor(encoder_out)



			prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(prompt)
			prompt_ids = tokenizer.encode(prompt_pre)
			prompt_length = len(prompt_ids)
			prompt_ids = torch.tensor(prompt_ids, dtype=torch.int64).to(kwargs["device"])


			if hasattr(self.llm.model, "embed_tokens"):
			inputs_embeds = self.llm.model.embed_tokens(prompt_ids)
			@@ -284,9 +296,13 @@
			else:
			inputs_embeds = self.llm.model.model.model.embed_tokens(prompt_ids)

			inputs_embeds = torch.cat((inputs_embeds[None, :, :], encoder_out), dim=1) # [prompt, audio]
			attention_mask = torch.ones(inputs_embeds.size()[:-1], dtype=torch.long).to(kwargs["device"])

			inputs_embeds = torch.cat(
			(inputs_embeds[None, :, :], encoder_out), dim=1
			) # [prompt, audio]
			attention_mask = torch.ones(inputs_embeds.size()[:-1], dtype=torch.long).to(
			kwargs["device"]
			)

			preds = self.llm.generate(
			inputs_embeds=inputs_embeds,
			max_length=kwargs.get("max_length", 200),
			@@ -301,17 +317,16 @@
			attention_mask=attention_mask,
			bos_token_id=tokenizer.bos_token_id,
			eos_token_id=tokenizer.eos_token_id,
			pad_token_id=tokenizer.pad_token_id
			pad_token_id=tokenizer.pad_token_id,
			)


			text = tokenizer.batch_decode(preds, add_special_tokens=False, skip_special_tokens=True)

			text = text[0].split(': ')[-1]
			text = text[0].split(": ")[-1]
			text = text.strip()


			# preds = torch.argmax(model_outputs.logits, -1)


			ibest_writer = None
			if kwargs.get("output_dir") is not None:
			if not hasattr(self, "writer"):
			@@ -324,9 +339,5 @@

			if ibest_writer is not None:
			ibest_writer["text"][key[0]] = text




			return results, meta_data

			return results, meta_data