python/FunASR-XL.git

			@@ -6,7 +6,7 @@
			import torch.nn as nn
			import torch.nn.functional as F
			from torch.cuda.amp import autocast

			import re
			from funasr.models.scama.utils import sequence_mask
			from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
			from funasr.models.ctc.ctc import CTC
			@@ -18,6 +18,8 @@
			from funasr.utils import postprocess_utils
			from funasr.utils.datadir_writer import DatadirWriter
			from funasr.register import tables
			from funasr.train_utils.device_funcs import to_device
			import traceback


			@tables.register("model_classes", "LLMASR")
			@@ -164,8 +166,6 @@
			text: (Batch, Length)
			text_lengths: (Batch,)
			"""
			# import pdb;
			# pdb.set_trace()
			if len(text_lengths.size()) > 1:
			text_lengths = text_lengths[:, 0]
			if len(speech_lengths.size()) > 1:
			@@ -480,14 +480,32 @@
			inputs_embeds = self.llm.model.get_input_embeddings()(input_ids)

			batch_size, token_num, dims = inputs_embeds.shape
			_, l, _ = encoder_out.shape
			fbank_mask[fbank_mask < 0] = 0
			fbank_fake_lens = fbank_mask.sum(-1).to(torch.int32)
			# _, l, _ = encoder_out.shape
			for batch_idx in range(batch_size):

			fbank_fake_len = fbank_fake_lens[batch_idx].item()
			fbank_beg_idx = fbank_beg[batch_idx, 0].item()
			min_len = min(l, inputs_embeds.shape[1] - fbank_beg_idx)
			inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
			batch_idx, :min_len, :
			]
			min_len = min(fbank_fake_len, inputs_embeds.shape[1] - fbank_beg_idx)

			try:
			inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
			batch_idx, :min_len, :
			]
			except Exception as e:
			logging.error(f"{str(e)}, {traceback.format_exc()}")
			logging.info(
			f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, min_len: {min_len}, fbank_fake_len: {fbank_fake_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens[batch_idx].item()}"
			)
			fbank_fake_len = encoder_out_lens[batch_idx].item()
			min_len = min(fbank_fake_len, min_len)
			inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
			batch_idx, :min_len, :
			]

			labels_ids[labels_ids == -1] = -100

			model_outputs = self.llm(
			inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
			)
			@@ -514,6 +532,133 @@
			loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
			return loss, stats, weight

			def data_template(self, data):
			system, user, assistant = [], [], []
			for i, item in enumerate(data):
			role = item["role"]
			content = item["content"]
			if role == "system":
			system.append(content)
			elif role == "user":
			user.append(content)
			elif role == "assistant":
			assistant.append(content)

			system = system * len(user)

			contents = {
			"system": system,
			"user": user,
			"assistant": assistant,
			}

			return contents

			def data_load_speech(self, contents: dict, tokenizer, frontend, meta_data={}, **kwargs):

			system = contents["system"]
			user = contents["user"]
			assistant = contents["assistant"]
			pattern = re.compile(r"(<\\|startofspeech\\|>.*?<\\|endofspeech\\|>)")
			input_ids, labels, source_ids, target_ids, fbank, fbank_lens, fbank_mask, fbank_beg = (
			[],
			[],
			[],
			[],
			[],
			[],
			[],
			[],
			)

			for i, (system_prompt, user_prompt, target_out) in enumerate(zip(system, user, assistant)):

			source_input = f"<\|im_start\|>system\n{system_prompt}<\|im_end\|>\n<\|im_start\|>user\n{user_prompt}<\|im_end\|>\n<\|im_start\|>assistant\n"

			splits = pattern.split(source_input)
			source_ids_i = []
			fbank_mask_i = []
			fbank_beg_i = []
			fbank_lens_i = []
			# target_ids_i = []
			for k, sub_str in enumerate(splits):
			if not sub_str.startswith("<\|startofspeech\|>"):
			sub_token = tokenizer.encode(sub_str)
			source_ids_i += sub_token
			fbank_mask_i += [0] * len(sub_token)
			else:
			sub_str = sub_str.replace("<\|startofspeech\|>", "").replace(
			"<\|endofspeech\|>", ""
			)
			if sub_str.startswith("!"):
			try:
			time1 = time.perf_counter()
			data_src = load_audio_text_image_video(sub_str[1:], fs=frontend.fs)
			time2 = time.perf_counter()
			meta_data["load_data"] = f"{time2 - time1:0.3f}"
			except Exception as e:
			logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}")

			speech, speech_lengths = extract_fbank(
			data_src,
			data_type=kwargs.get("data_type", "sound"),
			frontend=frontend,
			is_final=True,
			) # speech: [b, T, d]

			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
			)

			if kwargs.get("permute", True):
			speech = speech.permute(0, 2, 1)

			olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2
			olens = 1 + (olens - 3 + 2 * 1) // 2
			sub_token_len = (olens - 1) // 2 + 1
			sub_token = [0] * sub_token_len
			fbank_beg_i = [len(source_ids_i)]
			source_ids_i += sub_token
			fbank_mask_i += [1] * len(sub_token)

			source_mask = [-100] * len(source_ids_i)
			target_out = f"{target_out}<\|im_end\|>"
			target_ids = tokenizer.encode(target_out)
			input_ids += source_ids_i + target_ids
			labels += source_mask + target_ids
			fbank_mask += fbank_mask_i
			fbank_beg.append(fbank_beg_i)

			input_ids = torch.tensor(input_ids, dtype=torch.int64) # [: self.max_token_length]
			attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32)
			labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length]
			source_ids = torch.tensor(source_ids_i, dtype=torch.int64)
			target_ids = torch.tensor(target_ids, dtype=torch.int64)

			fbank = speech[0, :, :]
			fbank_lens = speech_lengths
			fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32)
			fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32)

			output = {
			"speech": fbank[None, :, :],
			"speech_lengths": fbank_lens[:, None],
			"fbank_mask": fbank_mask[None, :],
			"fbank_beg": fbank_beg[None,],
			"input_ids": input_ids[None, :],
			"attention_mask": attention_mask[None, :],
			"labels_ids": labels[None, :],
			"source_ids": source_ids[None, :],
			"target_ids": target_ids[None, :],
			}

			return output

			def inference(
			self,
			data_in,
			@@ -524,92 +669,86 @@
			**kwargs,
			):

			prompt = kwargs.get("prompt", "Transcribe speech to text.")
			meta_data = {}
			prompt = kwargs.get("prompt", None)

			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
			speech, speech_lengths = data_in, data_lengths
			if len(speech.shape) < 3:
			speech = speech[None, :, :]
			if speech_lengths is None:
			speech_lengths = speech.shape[1]
			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,
			)
			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
			)
			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
			)
			contents = self.data_template(data_in[0])
			output = self.data_load_speech(contents, tokenizer, frontend, meta_data=meta_data, **kwargs)
			batch = to_device(output, kwargs["device"])

			speech = speech.to(device=kwargs["device"])
			speech_lengths = speech_lengths.to(device=kwargs["device"])
			# audio encoder
			speech = batch["speech"]
			speech_lengths = batch["speech_lengths"][:, 0]
			# fp16
			if kwargs.get("fp16", False):
			speech = speech.to(torch.float16)
			encoder_out_lens = encoder_out_lens.to(torch.float16)
			elif kwargs.get("bf16", False):
			speech = speech.to(torch.bfloat16)
			encoder_out_lens = encoder_out_lens.to(torch.bfloat16)
			encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths)

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

			# adaptor
			encoder_out = self.audio_adaptor(encoder_out)
			input_ids = batch["input_ids"]
			source_ids = batch["source_ids"]
			if not kwargs.get("tearchforing", False):
			input_ids = source_ids
			input_ids[input_ids < 0] = 0
			inputs_embeds = self.llm.model.get_input_embeddings()(input_ids)

			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"])
			batch_size, token_num, dims = inputs_embeds.shape
			fbank_beg = batch["fbank_beg"]
			for batch_idx in range(batch_size):

			if hasattr(self.llm.model, "embed_tokens"):
			inputs_embeds = self.llm.model.embed_tokens(prompt_ids)
			elif hasattr(self.llm.model.model, "embed_tokens"):
			inputs_embeds = self.llm.model.model.embed_tokens(prompt_ids)
			else:
			inputs_embeds = self.llm.model.model.model.embed_tokens(prompt_ids)
			min_len = encoder_out_lens[batch_idx].item()
			fbank_beg_idx = fbank_beg[batch_idx]
			inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
			batch_idx, :min_len, :
			]

			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"]
			)
			llm_dtype = kwargs.get("llm_dtype", "fp32")
			dtype_map = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}
			with torch.cuda.amp.autocast(dtype=dtype_map[llm_dtype]):
			label = contents["assistant"][0]
			# self.llm = self.llm.to(dtype_map[llm_dtype])
			# inputs_embeds = inputs_embeds.to(dtype_map[llm_dtype])

			preds = self.llm.generate(
			inputs_embeds=inputs_embeds,
			max_length=kwargs.get("max_length", 200),
			max_new_tokens=kwargs.get("max_new_tokens", 200),
			num_beams=kwargs.get("num_beams", 4),
			do_sample=kwargs.get("do_sample", False),
			min_length=kwargs.get("min_length", 1),
			top_p=kwargs.get("top_p", 1.0),
			repetition_penalty=kwargs.get("repetition_penalty", 1.0),
			length_penalty=kwargs.get("length_penalty", 1.0),
			temperature=kwargs.get("temperature", 1.0),
			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,
			)
			if not kwargs.get("tearchforing", False):

			text = tokenizer.batch_decode(preds, add_special_tokens=False, skip_special_tokens=True)
			generated_ids = self.llm.generate(
			inputs_embeds=inputs_embeds, max_new_tokens=kwargs.get("max_length", 512)
			)
			# generated_ids = [
			# output_ids[len(input_id) :]
			# for input_id, output_ids in zip(input_ids, generated_ids)
			# ]
			response = tokenizer.batch_decode(
			generated_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True)
			)[0]

			text = text[0].split(": ")[-1]
			text = text.strip()
			loss = None
			else:

			# preds = torch.argmax(model_outputs.logits, -1)
			labels_ids = batch["labels_ids"]
			labels_ids[labels_ids == -1] = -100
			attention_mask = batch.get("attention_mask", None)
			# attention_mask = attention_mask.to(dtype_map[llm_dtype])
			model_outputs = self.llm(
			inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
			)

			preds = torch.argmax(model_outputs.logits, -1)[:, source_ids.shape[1] :]
			response = tokenizer.batch_decode(
			preds,
			add_special_tokens=False,
			skip_special_tokens=kwargs.get("skip_special_tokens", True),
			)[0]
			loss = model_outputs.loss.item()

			ibest_writer = None
			if kwargs.get("output_dir") is not None:
			@@ -618,10 +757,15 @@
			ibest_writer = self.writer[f"{0 + 1}best_recog"]

			results = []
			result_i = {"key": key[0], "text": text}
			response_clean = re.sub("[^\w\s\u3000\u4e00-\u9fff]+", "", response)
			result_i = {"key": key[0], "text": response, "text_tn": response_clean, "label": label}
			if loss is not None:
			result_i["loss"] = loss
			results.append(result_i)

			if ibest_writer is not None:
			ibest_writer["text"][key[0]] = text
			ibest_writer["text"][key[0]] = response
			ibest_writer["label"][key[0]] = label
			ibest_writer["text_tn"][key[0]] = response_clean

			return results, meta_data