From b52fc0ec2886c66c8b480a9f761a0af561afff5b Mon Sep 17 00:00:00 2001
From: Shi Xian <40013335+R1ckShi@users.noreply.github.com>
Date: 星期三, 28 二月 2024 14:39:54 +0800
Subject: [PATCH] Merge pull request #1401 from alibaba-damo-academy/dev_gzf
---
funasr/models/llm_asr_nar/model.py | 39 ++++++++++++++++++++++-----------------
1 files changed, 22 insertions(+), 17 deletions(-)
diff --git a/funasr/models/llm_asr/model.py b/funasr/models/llm_asr_nar/model.py
similarity index 90%
rename from funasr/models/llm_asr/model.py
rename to funasr/models/llm_asr_nar/model.py
index 2b6db96..a61190c 100644
--- a/funasr/models/llm_asr/model.py
+++ b/funasr/models/llm_asr_nar/model.py
@@ -294,24 +294,29 @@
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"])
- model_outputs = 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
- )
+ # model_outputs = 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
+ # )
- text = tokenizer.batch_decode(model_outputs, add_special_tokens=False, skip_special_tokens=True)
+
+ model_outputs = self.llm(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=None)
+ preds = torch.argmax(model_outputs.logits, -1)
+ text = tokenizer.batch_decode(preds, add_special_tokens=False, skip_special_tokens=True)
+ text = text.split(': "\n')[-1]
+ # preds = torch.argmax(model_outputs.logits, -1)
ibest_writer = None
if kwargs.get("output_dir") is not None:
--
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