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remove text norm

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root 2025-04-14 05:35:07 +00:00
parent 6b69276b19
commit 202d764cfb
1 changed files with 5 additions and 41 deletions
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46
egs/speech_llm/SPEECH2SPEECH/slam_omni/train.py
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@ -347,42 +347,6 @@ def compute_loss(
return input_ids, attention_mask, target_ids
def normalize_text_alimeeting(text: str, normalize: str = "m2met") -> str:
"""
Text normalization similar to M2MeT challenge baseline.
See: https://github.com/yufan-aslp/AliMeeting/blob/main/asr/local/text_normalize.pl
"""
if normalize == "none":
return text
elif normalize == "m2met":
import re
text = text.replace(" ", "")
text = text.replace("<sil>", "")
text = text.replace("<%>", "")
text = text.replace("<->", "")
text = text.replace("<$>", "")
text = text.replace("<#>", "")
text = text.replace("<_>", "")
text = text.replace("<space>", "")
text = text.replace("`", "")
text = text.replace("&", "")
text = text.replace(",", "")
if re.search("[a-zA-Z]", text):
text = text.upper()
text = text.replace("Ａ", "A")
text = text.replace("ａ", "A")
text = text.replace("ｂ", "B")
text = text.replace("ｃ", "C")
text = text.replace("ｋ", "K")
text = text.replace("ｔ", "T")
text = text.replace("，", "")
text = text.replace("丶", "")
text = text.replace("。", "")
text = text.replace("、", "")
text = text.replace("？", "")
return text
max_frames = params.max_duration * 1000 // params.frame_shift_ms
allowed_max_frames = int(max_frames * (1.0 + params.allowed_excess_duration_ratio))
batch = filter_uneven_sized_batch(batch, allowed_max_frames)
@ -400,15 +364,15 @@ def compute_loss(
questions_with_history = [cut.custom["question"] for cut in batch["supervisions"]["cut"]]
answer_cosyvoice_speech_token = [cut.custom["answer_cosyvoice_speech_token"] for cut in batch["supervisions"]["cut"]]
last_questions = [question.split('<USER>: ')[-1].strip() for question in questions_with_history]
texts = batch["supervisions"]["text"]
# remove spaces in texts
texts = [normalize_text_alimeeting(text) for text in texts]
history_contexts = [question.rsplit('<USER>:', 1)[0].strip() for question in questions_with_history]
# USER: 生成一个关于夏天的诗歌。 ASSISTANT: 夏日炎炎，万物生长，阳光明媚，享受着夏日的美好时光。 USER: 给我列举一些新闻头条。 ASSISTANT: 当今社会的新闻永远不会停。<USER>: 告诉我如何烹饪鸡肉
# <USER>: 对以下句子进行鉴赏：他心地善良。输出结果为“他是一个有善心的人。
messages = []
for i, text in enumerate(texts):
history_context = history_contexts[i]
message = [
{"role": "user", "content": f"{DEFAULT_SPEECH_TOKEN}请转写音频为文字"},
{"role": "user", "content": f"{history_context}{DEFAULT_SPEECH_TOKEN}请转写音频为文字"},
{"role": "assistant", "content": text},
]
messages.append(message)