mirror of
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672 lines
22 KiB
Python
Executable File
672 lines
22 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corporation (Author: Liyong Guo,
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# Fangjun Kuang,
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# Wei Kang)
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# 2024 Yuekai Zhang
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Usage:
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# Command for decoding using fine-tuned models:
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pip install huggingface_hub['cli']
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mkdir -p models/whisper models/qwen models/checkpoint
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huggingface-cli download --local-dir models/checkpoint yuekai/icefall_asr_aishell_whisper_qwen2_1.5B
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# For aishell fine-tuned whisper model
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huggingface-cli download --local-dir models/whisper yuekai/icefall_asr_aishell_whisper exp_large_v2/whisper-large-v2-aishell1-epoch-10-avg-6.pt
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# For multi-hans fine-tuned whisper model
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# huggingface-cli download --local-dir models/whisper yuekai/icefall_asr_multi-hans-zh_whisper v1.1/whisper-large-v2-multi-hans-zh-epoch-3-avg-10.pt
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huggingface-cli download --local-dir models/qwen Qwen/Qwen2-7B-Instruct
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mkdir -p whisper_llm_zh/exp_aishell_whisper_qwen2_1.5B
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ln -s models/checkpoint/epoch-10-avg-5.pt whisper_llm_zh/exp_aishell_whisper_qwen2_1.5B/epoch-999.pt
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python3 ./whisper_llm_zh/decode.py \
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--max-duration 80 \
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--exp-dir whisper_llm_zh/exp_aishell_whisper_qwen2_1.5B \
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--speech-encoder-path-or-name models/whisper/exp_large_v2/whisper-large-v2-aishell1-epoch-10-avg-6.pt \
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--llm-path-or-name models/qwen \
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--epoch 999 --avg 1 \
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--manifest-dir data/fbank \
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--use-flash-attn True \
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--use-lora True --dataset aishell
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"""
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import argparse
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import logging
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from collections import defaultdict
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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import torch
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import torch.nn as nn
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import transformers
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import whisper
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from data_module import AsrDataModule
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from lhotse.cut import Cut
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from model import SPEECH_LLM, EncoderProjector
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from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
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from train import DEFAULT_SPEECH_TOKEN
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
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from icefall.env import get_env_info
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from icefall.utils import (
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AttributeDict,
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setup_logger,
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store_transcripts,
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str2bool,
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write_error_stats,
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)
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def average_checkpoints(
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filenames: List[Path], device: torch.device = torch.device("cpu")
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) -> dict:
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"""Average a list of checkpoints.
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The function is mainly used for deepspeed converted checkpoint averaging, which only include model state_dict.
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Args:
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filenames:
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Filenames of the checkpoints to be averaged. We assume all
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checkpoints are saved by :func:`save_checkpoint`.
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device:
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Move checkpoints to this device before averaging.
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Returns:
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Return a dict (i.e., state_dict) which is the average of all
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model state dicts contained in the checkpoints.
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"""
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n = len(filenames)
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if "model" in torch.load(filenames[0], map_location=device):
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avg = torch.load(filenames[0], map_location=device)["model"]
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else:
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avg = torch.load(filenames[0], map_location=device)
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# Identify shared parameters. Two parameters are said to be shared
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# if they have the same data_ptr
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uniqued: Dict[int, str] = dict()
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for k, v in avg.items():
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v_data_ptr = v.data_ptr()
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if v_data_ptr in uniqued:
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continue
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uniqued[v_data_ptr] = k
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uniqued_names = list(uniqued.values())
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for i in range(1, n):
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if "model" in torch.load(filenames[i], map_location=device):
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state_dict = torch.load(filenames[i], map_location=device)["model"]
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else:
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state_dict = torch.load(filenames[i], map_location=device)
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for k in uniqued_names:
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avg[k] += state_dict[k]
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for k in uniqued_names:
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if avg[k].is_floating_point():
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avg[k] /= n
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else:
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avg[k] //= n
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return avg
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def add_model_arguments(parser: argparse.ArgumentParser):
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parser.add_argument(
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"--llm-path-or-name",
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type=str,
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default="",
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help="Path or name of the large language model.",
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)
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parser.add_argument(
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"--speech-encoder-path-or-name",
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type=str,
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default="whisper-large-v2",
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help="Path or name of the speech encoder.",
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)
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parser.add_argument(
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"--encoder-projector-ds-rate",
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type=int,
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default=8,
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help="Downsample rate for the encoder projector.",
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)
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parser.add_argument(
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"--use-flash-attn",
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type=str2bool,
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default=True,
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help="Whether to use flash attention.",
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)
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parser.add_argument(
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"--use-lora",
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type=str2bool,
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default=True,
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help="Whether to use lora fine-tuned llm checkpoint.",
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)
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def get_parser():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--epoch",
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type=int,
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default=-1,
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help="It specifies the checkpoint to use for decoding."
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"Note: Epoch counts from 0.",
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)
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parser.add_argument(
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"--avg",
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type=int,
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default=1,
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help="Number of checkpoints to average. Automatically select "
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"consecutive checkpoints before the checkpoint specified by "
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"'--epoch'. ",
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)
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parser.add_argument(
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"--method",
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type=str,
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default="beam-search",
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help="""Decoding method.
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Supported values are:
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- beam-search
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""",
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)
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parser.add_argument(
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"--beam-size",
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type=int,
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default=1,
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help="beam size for beam search decoding",
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)
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parser.add_argument(
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"--exp-dir",
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type=str,
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default="whisper/exp",
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help="The experiment dir",
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)
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parser.add_argument(
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"--remove-whisper-encoder-input-length-restriction",
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type=str2bool,
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default=True,
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help="replace whisper encoder forward method to remove input length restriction",
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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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# default="aishell",
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# choices=["aishell", "speechio", "wenetspeech_test_meeting", "multi_hans_zh"],
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# help="The dataset to decode",
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# )
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add_model_arguments(parser)
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return parser
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def get_params() -> AttributeDict:
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params = AttributeDict(
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{
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"env_info": get_env_info(),
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}
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)
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return params
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def decode_one_batch(
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params: AttributeDict,
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model: nn.Module,
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tokenizer: AutoTokenizer,
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batch: dict,
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) -> Dict[str, List[List[int]]]:
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"""Decode one batch and return the result in a dict. The dict has the
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following format:
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- key: "beam-search"
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- value: A list of lists. Each sublist is a list of token IDs.
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Args:
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params:
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It is returned by :func:`get_params`.
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model:
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The neural model.
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batch:
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It is returned by :meth:`torch.utils.data.DataLoader.__iter__`.
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Returns:
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Return a dict, whose key may be "beam-search".
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"""
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def preprocess(
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messages,
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tokenizer: transformers.PreTrainedTokenizer,
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) -> Dict:
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"""Preprocesses the data for supervised fine-tuning."""
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texts = []
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TEMPLATE = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content']}}{% if loop.last %}{{''}}{% else %}{{ '<|im_end|>\n' }}{% endif %}{% endfor %}"
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for i, msg in enumerate(messages):
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texts.append(
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tokenizer.apply_chat_template(
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msg,
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tokenize=True,
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add_generation_prompt=False,
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chat_template=TEMPLATE,
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padding="longest",
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truncation=False,
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)
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)
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max_len_texts = max([len(text) for text in texts])
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if tokenizer.padding_side == "right":
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texts = [
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text + [tokenizer.pad_token_id] * (max_len_texts - len(text))
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for text in texts
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]
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else:
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texts = [
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[tokenizer.pad_token_id] * (max_len_texts - len(text)) + text
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for text in texts
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]
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input_ids = torch.tensor(texts, dtype=torch.int)
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attention_mask = input_ids.ne(tokenizer.pad_token_id)
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return input_ids, attention_mask
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dtype = torch.float32
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device = model.llm.device
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feature = batch["inputs"]
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assert feature.ndim == 3
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feature = feature.to(device, dtype=dtype).transpose(1, 2)
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if not params.remove_whisper_encoder_input_length_restriction:
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T = 3000
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if feature.shape[2] < T:
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feature = torch.cat(
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[
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feature,
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torch.zeros(
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feature.shape[0], feature.shape[1], T - feature.shape[2]
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).to(device, dtype=dtype),
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],
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2,
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)
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chat_rounds = [cut.custom["round"] for cut in batch["supervisions"]["cut"]]
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# messages = [
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# [
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# {"role": "user", "content": f"{DEFAULT_SPEECH_TOKEN}请转写音频为文字"},
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# {"role": "assistant", "content": ""},
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# ]
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# ] * len(feature)
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questions_with_history = [cut.custom["question"] for cut in batch["supervisions"]["cut"]]
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history_contexts = [question.rsplit('<USER>:', 1)[0].strip() for question in questions_with_history]
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last_questions = [question.split('<USER>: ')[-1].strip() for question in questions_with_history]
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messages = []
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for i, total_round in enumerate(chat_rounds):
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message = []
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if total_round > 1:
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history_question_answer = history_contexts[i].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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# 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。
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question_answer = history_question_answer[j].split('ASSISTANT:')
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message += [
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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 += [
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{"role": "user", "content": f"{DEFAULT_SPEECH_TOKEN}"},
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# {"role": "user", "content": f"{last_questions[i]}"},
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{"role": "assistant", "content": ""}
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]
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print(f"message: {message}, batch_size {len(chat_rounds)}")
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messages.append(message)
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input_ids, attention_mask = preprocess(messages, tokenizer)
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generated_ids = model.decode(
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feature, input_ids.to(device, dtype=torch.long), attention_mask.to(device)
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)
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hyps = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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return {"beam-search": hyps}
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def decode_dataset(
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dl: torch.utils.data.DataLoader,
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params: AttributeDict,
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model: nn.Module,
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tokenizer: AutoTokenizer,
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) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
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"""Decode dataset.
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Args:
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dl:
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The dataloader.
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params:
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It is returned by :func:`get_params`.
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model:
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The neural model.
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Returns:
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Return a dict, whose key may be "beam-search".
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"""
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def normalize_text_alimeeting(text: str, normalize: str = "m2met") -> str:
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"""
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Text normalization similar to M2MeT challenge baseline.
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See: https://github.com/yufan-aslp/AliMeeting/blob/main/asr/local/text_normalize.pl
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"""
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if normalize == "none":
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return text
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elif normalize == "m2met":
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import re
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text = text.replace(" ", "")
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text = text.replace("<sil>", "")
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text = text.replace("<%>", "")
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text = text.replace("<->", "")
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text = text.replace("<$>", "")
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text = text.replace("<#>", "")
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text = text.replace("<_>", "")
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text = text.replace("<space>", "")
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text = text.replace("`", "")
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text = text.replace("&", "")
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text = text.replace(",", "")
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if re.search("[a-zA-Z]", text):
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text = text.upper()
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text = text.replace("Ａ", "A")
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text = text.replace("ａ", "A")
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text = text.replace("ｂ", "B")
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text = text.replace("ｃ", "C")
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text = text.replace("ｋ", "K")
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text = text.replace("ｔ", "T")
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text = text.replace("，", "")
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text = text.replace("丶", "")
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text = text.replace("。", "")
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text = text.replace("、", "")
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text = text.replace("？", "")
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return text
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results = []
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num_cuts = 0
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try:
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num_batches = len(dl)
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except TypeError:
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num_batches = "?"
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results = defaultdict(list)
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for batch_idx, batch in enumerate(dl):
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answers = batch["supervisions"]["text"]
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questions_with_history = [cut.custom["question"] for cut in batch["supervisions"]["cut"]]
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answer_cosyvoice_speech_token = [cut.custom["answer_cosyvoice_speech_token"] for cut in batch["supervisions"]["cut"]]
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texts = [question.split('<USER>: ')[-1].strip() for question in questions_with_history]
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cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
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hyps_dict = decode_one_batch(
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params=params,
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model=model,
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batch=batch,
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tokenizer=tokenizer,
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)
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for lm_scale, hyps in hyps_dict.items():
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this_batch = []
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assert len(hyps) == len(texts)
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for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
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# ref_text = normalize_text_alimeeting(ref_text)
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ref_words = ref_text.split()
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print(f"ref: {ref_text}")
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print(f"hyp: {''.join(hyp_words)}")
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this_batch.append((cut_id, ref_words, hyp_words))
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results[lm_scale].extend(this_batch)
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num_cuts += len(batch["supervisions"]["text"])
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if batch_idx % 100 == 0:
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batch_str = f"{batch_idx}/{num_batches}"
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logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
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return results
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def save_results(
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params: AttributeDict,
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test_set_name: str,
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results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
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):
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enable_log = True
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test_set_wers = dict()
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for key, results in results_dict.items():
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recog_path = (
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params.log_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
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)
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results = sorted(results)
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store_transcripts(filename=recog_path, texts=results)
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if enable_log:
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logging.info(f"The transcripts are stored in {recog_path}")
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# The following prints out WERs, per-word error statistics and aligned
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# ref/hyp pairs.
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errs_filename = (
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params.log_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
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)
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# we compute CER for aishell dataset.
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results_char = []
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for res in results:
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results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
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with open(errs_filename, "w") as f:
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wer = write_error_stats(
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f, f"{test_set_name}-{key}", results_char, enable_log=enable_log
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)
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test_set_wers[key] = wer
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if enable_log:
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logging.info("Wrote detailed error stats to {}".format(errs_filename))
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test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
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errs_info = params.log_dir / f"cer-summary-{test_set_name}-{params.suffix}.txt"
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with open(errs_info, "w") as f:
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print("settings\tCER", file=f)
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for key, val in test_set_wers:
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print("{}\t{}".format(key, val), file=f)
|
||
|
||
s = "\nFor {}, CER of different settings are:\n".format(test_set_name)
|
||
note = "\tbest for {}".format(test_set_name)
|
||
for key, val in test_set_wers:
|
||
s += "{}\t{}{}\n".format(key, val, note)
|
||
note = ""
|
||
logging.info(s)
|
||
|
||
|
||
@torch.no_grad()
|
||
def main():
|
||
parser = get_parser()
|
||
AsrDataModule.add_arguments(parser)
|
||
args = parser.parse_args()
|
||
args.exp_dir = Path(args.exp_dir)
|
||
|
||
params = get_params()
|
||
params.update(vars(args))
|
||
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
|
||
params.log_dir = Path(params.exp_dir) / f"log-{params.method}"
|
||
params.log_dir.mkdir(parents=True, exist_ok=True)
|
||
setup_logger(
|
||
f"{params.exp_dir}/log-{params.method}/log-decode-{params.suffix}"
|
||
)
|
||
|
||
logging.info("Decoding started")
|
||
logging.info(params)
|
||
|
||
device = torch.device("cpu")
|
||
if torch.cuda.is_available():
|
||
device = torch.device("cuda")
|
||
|
||
logging.info(f"device: {device}")
|
||
|
||
if params.remove_whisper_encoder_input_length_restriction:
|
||
replace_whisper_encoder_forward()
|
||
|
||
whisper_model = whisper.load_model(params.speech_encoder_path_or_name, "cpu")
|
||
speech_encoder = whisper_model.encoder
|
||
speech_encoder_dim = whisper_model.dims.n_audio_state
|
||
tokenizer = AutoTokenizer.from_pretrained(params.llm_path_or_name)
|
||
|
||
if params.use_flash_attn:
|
||
attn_implementation = "flash_attention_2"
|
||
# torch_dtype=torch.bfloat16 FIX ME
|
||
torch_dtype = torch.float16
|
||
tokenizer.padding_side = "left"
|
||
|
||
else:
|
||
attn_implementation = "eager"
|
||
torch_dtype = torch.float16
|
||
tokenizer.padding_side = "right"
|
||
|
||
llm = AutoModelForCausalLM.from_pretrained(
|
||
params.llm_path_or_name,
|
||
attn_implementation=attn_implementation,
|
||
torch_dtype=torch_dtype,
|
||
)
|
||
if params.use_lora:
|
||
lora_config = LoraConfig(
|
||
r=64,
|
||
lora_alpha=16,
|
||
target_modules=[
|
||
"q_proj",
|
||
"k_proj",
|
||
"v_proj",
|
||
"o_proj",
|
||
"up_proj",
|
||
"gate_proj",
|
||
"down_proj",
|
||
],
|
||
task_type="CAUSAL_LM",
|
||
)
|
||
llm = get_peft_model(llm, lora_config)
|
||
llm.print_trainable_parameters()
|
||
|
||
special_tokens_dict = {"additional_special_tokens": [DEFAULT_SPEECH_TOKEN]}
|
||
tokenizer.add_special_tokens(special_tokens_dict)
|
||
llm.config.pad_token_id = tokenizer.convert_tokens_to_ids("<|endoftext|>")
|
||
llm.config.bos_token_id = tokenizer.convert_tokens_to_ids("<|im_start|>")
|
||
llm.config.eos_token_id = tokenizer.convert_tokens_to_ids("<|im_end|>")
|
||
|
||
llm.config.default_speech_token_id = tokenizer.convert_tokens_to_ids(
|
||
DEFAULT_SPEECH_TOKEN
|
||
)
|
||
|
||
encoder_projector = EncoderProjector(
|
||
speech_encoder_dim, llm.config.hidden_size, params.encoder_projector_ds_rate
|
||
)
|
||
|
||
model = SPEECH_LLM(
|
||
speech_encoder,
|
||
llm,
|
||
encoder_projector,
|
||
)
|
||
|
||
if params.avg > 1:
|
||
start = params.epoch - params.avg + 1
|
||
assert start >= 1, start
|
||
checkpoint = torch.load(
|
||
f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
|
||
)
|
||
assert "model" not in checkpoint
|
||
# deepspeed converted checkpoint only contains model state_dict
|
||
filenames = [
|
||
f"{params.exp_dir}/epoch-{epoch}.pt"
|
||
for epoch in range(start, params.epoch + 1)
|
||
]
|
||
avg_checkpoint = average_checkpoints(filenames)
|
||
model.load_state_dict(avg_checkpoint, strict=False)
|
||
|
||
filename = f"{params.exp_dir}/epoch-{params.epoch}-avg-{params.avg}.pt"
|
||
torch.save(avg_checkpoint, filename)
|
||
else:
|
||
checkpoint = torch.load(
|
||
f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
|
||
)
|
||
model.load_state_dict(checkpoint, strict=False)
|
||
|
||
model.to(device)
|
||
model.eval()
|
||
num_param = sum([p.numel() for p in model.parameters()])
|
||
logging.info(f"Number of model parameters: {num_param}")
|
||
|
||
# we need cut ids to display recognition results.
|
||
args.return_cuts = True
|
||
|
||
data_module = AsrDataModule(args)
|
||
# data_module = MultiDataset(args.manifest_dir)
|
||
|
||
def remove_long_utt(c: Cut):
|
||
# Keep only utterances with duration in 30 seconds
|
||
#
|
||
if c.duration > 30.0:
|
||
logging.warning(
|
||
f"Exclude cut with ID {c.id} from training. Duration: {c.duration}"
|
||
)
|
||
return False
|
||
return True
|
||
|
||
# if params.dataset == "aishell":
|
||
# test_sets_cuts = data_module.aishell_test_cuts()
|
||
# elif params.dataset == "speechio":
|
||
# test_sets_cuts = data_module.speechio_test_cuts()
|
||
# elif params.dataset == "wenetspeech_test_meeting":
|
||
# test_sets_cuts = data_module.wenetspeech_test_meeting_cuts()
|
||
# else:
|
||
test_sets_cuts = data_module.test_cuts()
|
||
|
||
test_sets = test_sets_cuts.keys()
|
||
test_dls = [
|
||
data_module.test_dataloaders(test_sets_cuts[cuts_name].filter(remove_long_utt))
|
||
for cuts_name in test_sets
|
||
]
|
||
|
||
for test_set, test_dl in zip(test_sets, test_dls):
|
||
results_dict = decode_dataset(
|
||
dl=test_dl,
|
||
params=params,
|
||
model=model,
|
||
tokenizer=tokenizer,
|
||
)
|
||
|
||
save_results(params=params, test_set_name=test_set, results_dict=results_dict)
|
||
|
||
logging.info("Done!")
|
||
|
||
|
||
torch.set_num_threads(1)
|
||
torch.set_num_interop_threads(1)
|
||
|
||
if __name__ == "__main__":
|
||
main()
|