diff --git a/egs/speech_llm/ASR_LLM/README.md b/egs/speech_llm/ASR_LLM/README.md new file mode 100644 index 000000000..1e60c733c --- /dev/null +++ b/egs/speech_llm/ASR_LLM/README.md @@ -0,0 +1,39 @@ + +# Introduction + +This recipe includes scripts for training Zipformer model using multiple Chinese datasets. + +# Included Training Sets +1. THCHS-30 +2. AiShell-{1,2,4} +3. ST-CMDS +4. Primewords +5. MagicData +6. Aidatatang_200zh +7. AliMeeting +8. WeNetSpeech +9. KeSpeech-ASR + +|Datset| Number of hours| URL| +|---|---:|---| +|**TOTAL**|14,106|---| +|THCHS-30|35|https://www.openslr.org/18/| +|AiShell-1|170|https://www.openslr.org/33/| +|AiShell-2|1,000|http://www.aishelltech.com/aishell_2| +|AiShell-4|120|https://www.openslr.org/111/| +|ST-CMDS|110|https://www.openslr.org/38/| +|Primewords|99|https://www.openslr.org/47/| +|aidatatang_200zh|200|https://www.openslr.org/62/| +|MagicData|755|https://www.openslr.org/68/| +|AliMeeting|100|https://openslr.org/119/| +|WeNetSpeech|10,000|https://github.com/wenet-e2e/WenetSpeech| +|KeSpeech|1,542|https://github.com/KeSpeech/KeSpeech| + + +# Included Test Sets +1. Aishell-{1,2,4} +2. Aidatatang_200zh +3. AliMeeting +4. MagicData +5. KeSpeech-ASR +6. WeNetSpeech diff --git a/egs/speech_llm/ASR_LLM/RESULTS.md b/egs/speech_llm/ASR_LLM/RESULTS.md new file mode 100644 index 000000000..a7f3bc4f7 --- /dev/null +++ b/egs/speech_llm/ASR_LLM/RESULTS.md @@ -0,0 +1,116 @@ +## Results + +### Multi Chinese datasets (without datatang 200h) finetuning results on Whisper-large-v2 +#### Whisper +[./whisper](./whisper) + +Character Error Rates (CERs) listed below are produced by the checkpoint of the second epoch using greedy search. + +| Datasets | alimeeting | alimeeting | aishell-1 | aishell-1 | aishell-2 | aishell-2 | aishell-4 | magicdata | magicdata | kespeech-asr | kespeech-asr | kespeech-asr | WenetSpeech | +|--------------------------------|-------------------|--------------|----------------|-------------|------------------|-------------|------------------|------------------|-------------|-----------------------|-----------------------|-------------|-------------------| +| Split | eval| test | dev | test | dev| test | test | dev| test | dev phase1 | dev phase2 | test | test meeting | +| Greedy Search | 23.22 | 28.24 | 0.61 | 0.66 | 2.67 | 2.80 | 16.61 | 2.56 | 2.21 | 4.73 | 1.90 | 5.98 | 8.13 | + +Command for training is: +```bash +pip install -r whisper/requirements.txt + +# We updated the label of wenetspeech to remove OCR deletion errors, see https://github.com/wenet-e2e/WenetSpeech/discussions/54 + +torchrun --nproc-per-node 8 ./whisper/train.py \ + --max-duration 200 \ + --exp-dir whisper/exp_large_v2 \ + --model-name large-v2 \ + --deepspeed \ + --deepspeed_config ./whisper/ds_config_zero1.json +``` + +Command for decoding using fine-tuned models: +```bash +git lfs install +git clone https://huggingface.co/yuekai/icefall_asr_multi-hans-zh_whisper +ln -s icefall_asr_multi-hans-zh_whisper/v1.1/epoch-3-avg-10.pt whisper/exp_large_v2/epoch-999.pt + +python3 ./whisper/decode.py \ + --exp-dir whisper/exp_large_v2 \ + --model-name large-v2 \ + --epoch 999 --avg 1 \ + --beam-size 10 --max-duration 50 +``` + +Fine-tuned models, training logs, decoding logs, tensorboard and decoding results +are available at + + + +### Multi Chinese datasets char-based training results (Non-streaming) on zipformer model + +This is the [pull request #1238](https://github.com/k2-fsa/icefall/pull/1238) in icefall. + +#### Non-streaming (with CTC head) + +Best results (num of params : ~69M): + +The training command: + +``` +./zipformer/train.py \ + --world-size 4 \ + --num-epochs 20 \ + --use-fp16 1 \ + --max-duration 600 \ + --num-workers 8 \ + --use-ctc 1 +``` + +The decoding command: + +``` +./zipformer/decode.py \ + --epoch 20 \ + --avg 1 \ + --use-ctc 1 +``` + +Character Error Rates (CERs) listed below are produced by the checkpoint of the 20th epoch using BPE model ( # tokens is 2000, byte fallback enabled). + +| Datasets | aidatatang _200zh | aidatatang _200zh | alimeeting | alimeeting | aishell-1 | aishell-1 | aishell-2 | aishell-2 | aishell-4 | magicdata | magicdata | kespeech-asr | kespeech-asr | kespeech-asr | WenetSpeech | WenetSpeech | WenetSpeech | +|--------------------------------|------------------------------|-------------|-------------------|--------------|----------------|-------------|------------------|-------------|------------------|------------------|-------------|-----------------------|-----------------------|-------------|--------------------|-------------------------|---------------------| +| Zipformer CER (%) | dev | test | eval | test | dev | test | dev | test | test | dev | test | dev phase1 | dev phase2 | test | dev | test meeting | test net | +| CTC Decoding | 2.86 | 3.36 | 22.93 | 24.28 | 2.05 | 2.27 | 3.33 | 3.82 | 15.45 | 3.49 | 2.77 | 6.90 | 2.85 | 8.29 | 9.41 | 6.92 | 8.57 | +| Greedy Search | 3.36 | 3.83 | 23.90 | 25.18 | 2.77 | 3.08 | 3.70 | 4.04 | 16.13 | 3.77 | 3.15 | 6.88 | 3.14 | 8.08 | 9.04 | 7.19 | 8.17 | + +Pre-trained model can be found here : https://huggingface.co/zrjin/icefall-asr-multi-zh-hans-zipformer-ctc-2023-10-24/ + +#### Non-streaming + +Best results (num of params : ~69M): + +The training command: + +``` +./zipformer/train.py \ + --world-size 4 \ + --num-epochs 20 \ + --use-fp16 1 \ + --max-duration 600 \ + --num-workers 8 +``` + +The decoding command: + +``` +./zipformer/decode.py \ + --epoch 20 \ + --avg 1 +``` + +Character Error Rates (CERs) listed below are produced by the checkpoint of the 20th epoch using greedy search and BPE model ( # tokens is 2000, byte fallback enabled). + +| Datasets | aidatatang _200zh | aidatatang _200zh | alimeeting | alimeeting | aishell-1 | aishell-1 | aishell-2 | aishell-2 | aishell-4 | magicdata | magicdata | kespeech-asr | kespeech-asr | kespeech-asr | WenetSpeech | WenetSpeech | WenetSpeech | +|--------------------------------|------------------------------|-------------|-------------------|--------------|----------------|-------------|------------------|-------------|------------------|------------------|-------------|-----------------------|-----------------------|-------------|--------------------|-------------------------|---------------------| +| Zipformer CER (%) | dev | test | eval| test | dev | test | dev| test | test | dev| test | dev phase1 | dev phase2 | test | dev | test meeting | test net | +| Greedy Search | 3.2 | 3.67 | 23.15 | 24.78 | 2.91 | 3.04 | 3.59 | 4.03 | 15.68 | 3.68 | 3.12 | 6.69 | 3.19 | 8.01 | 9.32 | 7.05 | 8.78 | + + +Pre-trained model can be found here : https://huggingface.co/zrjin/icefall-asr-multi-zh-hans-zipformer-2023-9-2/ diff --git a/egs/speech_llm/ASR_LLM/run.sh b/egs/speech_llm/ASR_LLM/run.sh new file mode 100755 index 000000000..7a6c39631 --- /dev/null +++ b/egs/speech_llm/ASR_LLM/run.sh @@ -0,0 +1,15 @@ + +export PYTHONPATH=$PYTHONPATH:/workspace/asr/icefall +# pip install k2==1.24.3.dev20230524+cuda11.8.torch2.0.1 -f https://k2-fsa.github.io/k2/cuda.html +# pip install -r whisper/requirements.txt + +method=mask_predict +# method=cif_ar_distill_embedding +torchrun --nproc_per_node 8 ./parawhisper/train.py \ + --max-duration 200 \ + --exp-dir parawhisper/exp_large_v2_${method} \ + --model-name large-v2 \ + --manifest-dir data/fbank \ + --method $method \ + --deepspeed \ + --deepspeed_config ./whisper/ds_config_zero1.json \ No newline at end of file diff --git a/egs/speech_llm/ASR_LLM/whisper_llm_zh/asr_datamodule.py b/egs/speech_llm/ASR_LLM/whisper_llm_zh/asr_datamodule.py new file mode 120000 index 000000000..816a4bf01 --- /dev/null +++ b/egs/speech_llm/ASR_LLM/whisper_llm_zh/asr_datamodule.py @@ -0,0 +1 @@ +../../../multi_zh-hans/ASR/zipformer/asr_datamodule.py \ No newline at end of file diff --git a/egs/speech_llm/ASR_LLM/whisper_llm_zh/decode.py b/egs/speech_llm/ASR_LLM/whisper_llm_zh/decode.py new file mode 100755 index 000000000..f758f546c --- /dev/null +++ b/egs/speech_llm/ASR_LLM/whisper_llm_zh/decode.py @@ -0,0 +1,567 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corporation (Author: Liyong Guo, +# Fangjun Kuang, +# Wei Kang) +# 2024 Yuekai Zhang +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: +# Command for decoding using fine-tuned models: +git lfs install +git clone https://huggingface.co/yuekai/icefall_asr_aishell_whisper +ln -s icefall_asr_aishell_whisper/exp_large_v2/epoch-10-avg6.pt whisper/exp_large_v2/epoch-999.pt + +python3 ./whisper/decode.py \ + --exp-dir whisper/exp_large_v2 \ + --model-name large-v2 \ + --epoch 999 --avg 1 \ + --beam-size 10 --max-duration 50 + +# Command for decoding using pretrained models (before fine-tuning): + +python3 ./whisper/decode.py \ + --exp-dir whisper/exp_large_v2 \ + --model-name large-v2 \ + --epoch -1 --avg 1 \ + --remove-whisper-encoder-input-length-restriction False \ + --beam-size 10 --max-duration 50 + +""" + +import argparse +import logging +import re +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import torch +import torch.nn as nn +import whisper +from asr_datamodule import AsrDataModule +from lhotse.cut import Cut +from multi_dataset import MultiDataset +from tn.chinese.normalizer import Normalizer +from whisper.normalizers import BasicTextNormalizer +from whisper_decoder_forward_monkey_patch import replace_whisper_decoder_forward +from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward +from zhconv import convert + +from icefall.checkpoint import average_checkpoints_with_averaged_model, load_checkpoint +from icefall.env import get_env_info +from icefall.utils import ( + AttributeDict, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + + +def average_checkpoints( + filenames: List[Path], device: torch.device = torch.device("cpu") +) -> dict: + """Average a list of checkpoints. + The function is mainly used for deepspeed converted checkpoint averaging, which only include model state_dict. + + Args: + filenames: + Filenames of the checkpoints to be averaged. We assume all + checkpoints are saved by :func:`save_checkpoint`. + device: + Move checkpoints to this device before averaging. + Returns: + Return a dict (i.e., state_dict) which is the average of all + model state dicts contained in the checkpoints. + """ + n = len(filenames) + + if "model" in torch.load(filenames[0], map_location=device): + avg = torch.load(filenames[0], map_location=device)["model"] + else: + avg = torch.load(filenames[0], map_location=device) + + # Identify shared parameters. Two parameters are said to be shared + # if they have the same data_ptr + uniqued: Dict[int, str] = dict() + + for k, v in avg.items(): + v_data_ptr = v.data_ptr() + if v_data_ptr in uniqued: + continue + uniqued[v_data_ptr] = k + + uniqued_names = list(uniqued.values()) + + for i in range(1, n): + if "model" in torch.load(filenames[i], map_location=device): + state_dict = torch.load(filenames[i], map_location=device)["model"] + else: + state_dict = torch.load(filenames[i], map_location=device) + for k in uniqued_names: + avg[k] += state_dict[k] + + for k in uniqued_names: + if avg[k].is_floating_point(): + avg[k] /= n + else: + avg[k] //= n + + return avg + + +def remove_punctuation(text: str or List[str]): + """Modified from https://github.com/yeyupiaoling/Whisper-Finetune/blob/master/utils/data_utils.py + + Args: + text: It can be a string or a list of strings. + Returns: + Return a string or a list of strings without any punctuation. + """ + punctuation = "!,.;:?、！，。；：？《》 " + if isinstance(text, str): + text = re.sub(r"[{}]+".format(punctuation), "", text).strip() + return text + elif isinstance(text, list): + result_text = [] + for t in text: + t = re.sub(r"[{}]+".format(punctuation), "", t).strip() + result_text.append(t) + return result_text + else: + raise Exception(f"Not support type {type(text)}") + + +def to_simple(text: str or List[str]): + """Convert traditional Chinese to simplified Chinese. + Args: + text: It can be a string or a list of strings. + Returns: + Return a string or a list of strings converted to simplified Chinese. + """ + if isinstance(text, str): + text = convert(text, "zh-cn") + return text + elif isinstance(text, list): + result_text = [] + for t in text: + t = convert(t, "zh-cn") + result_text.append(t) + return result_text + else: + raise Exception(f"Not support type{type(text)}") + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=-1, + help="It specifies the checkpoint to use for decoding." + "Note: Epoch counts from 0.", + ) + parser.add_argument( + "--avg", + type=int, + default=1, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch'. ", + ) + + parser.add_argument( + "--method", + type=str, + default="beam-search", + help="""Decoding method. + Supported values are: + - beam-search + """, + ) + + parser.add_argument( + "--beam-size", + type=int, + default=1, + help="beam size for beam search decoding", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="whisper/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--model-name", + type=str, + default="large-v2", + choices=["large-v2", "large-v3", "medium", "base", "small", "tiny"], + help="""The model name to use. + """, + ) + + parser.add_argument( + "--remove-whisper-encoder-input-length-restriction", + type=str2bool, + default=True, + help="replace whisper encoder forward method to remove input length restriction", + ) + + parser.add_argument( + "--use-distill-whisper", + type=str2bool, + default=False, + help="Whether to use architecture of distill whisper.", + ) + + return parser + + +def get_params() -> AttributeDict: + params = AttributeDict( + { + "env_info": get_env_info(), + } + ) + return params + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + batch: dict, +) -> Dict[str, List[List[int]]]: + """Decode one batch and return the result in a dict. The dict has the + following format: + + - key: "beam-search" + - value: A list of lists. Each sublist is a list of token IDs. + Args: + params: + It is returned by :func:`get_params`. + model: + The neural model. + batch: + It is returned by :meth:`torch.utils.data.DataLoader.__iter__`. + Returns: + Return a dict, whose key may be "beam-search". + """ + dtype = torch.float16 + device = torch.device("cuda") + + feature = batch["inputs"] + assert feature.ndim == 3 + feature = feature.to(device, dtype=dtype).transpose(1, 2) + if not params.remove_whisper_encoder_input_length_restriction: + T = 3000 + if feature.shape[2] < T: + feature = torch.cat( + [ + feature, + torch.zeros( + feature.shape[0], feature.shape[1], T - feature.shape[2] + ).to(device, dtype=dtype), + ], + 2, + ) + + supervisions = batch["supervisions"] + feature_len = supervisions["num_frames"] + feature_len = feature_len.to(device, dtype=dtype) + results = model.decode(feature, params.decoding_options) + hyps = [result.text for result in results] + + hyps = remove_punctuation(hyps) + hyps = to_simple(hyps) + hyps = [params.normalizer.normalize(hyp) for hyp in hyps] + print(hyps) + return {"beam-search": hyps} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, +) -> Dict[str, List[Tuple[str, List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + The dataloader. + params: + It is returned by :func:`get_params`. + model: + The neural model. + Returns: + Return a dict, whose key may be "beam-search". + """ + + 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("", "") + text = text.replace("<%>", "") + text = text.replace("<->", "") + text = text.replace("<$>", "") + text = text.replace("<#>", "") + text = text.replace("<_>", "") + text = text.replace("", "") + 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 + + results = [] + + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + cut_ids = [cut.id for cut in batch["supervisions"]["cut"]] + + hyps_dict = decode_one_batch( + params=params, + model=model, + batch=batch, + ) + + for lm_scale, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts): + ref_text = normalize_text_alimeeting(ref_text) + ref_words = ref_text.split() + this_batch.append((cut_id, ref_words, hyp_words)) + + results[lm_scale].extend(this_batch) + + num_cuts += len(batch["supervisions"]["text"]) + + if batch_idx % 100 == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}") + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]], +): + + enable_log = True + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.exp_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + results = sorted(results) + store_transcripts(filename=recog_path, texts=results) + if enable_log: + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.exp_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + # we compute CER for aishell dataset. + results_char = [] + for res in results: + results_char.append((res[0], list("".join(res[1])), list("".join(res[2])))) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results_char, enable_log=enable_log + ) + test_set_wers[key] = wer + + if enable_log: + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = params.exp_dir / f"cer-summary-{test_set_name}-{params.suffix}.txt" + with open(errs_info, "w") as f: + print("settings\tCER", file=f) + for key, val in test_set_wers: + 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}" + setup_logger( + f"{params.exp_dir}/log-{params.method}-beam{params.beam_size}/log-decode-{params.suffix}" + ) + + options = whisper.DecodingOptions( + task="transcribe", + language="zh", + without_timestamps=True, + beam_size=params.beam_size, + ) + params.decoding_options = options + params.cleaner = BasicTextNormalizer() + params.normalizer = Normalizer() + + 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() + if params.use_distill_whisper: + replace_whisper_decoder_forward() + model = whisper.load_model(params.model_name, "cpu") + if params.epoch > 0: + if params.avg > 1: + start = params.epoch - params.avg + assert start >= 1, start + checkpoint = torch.load( + f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu" + ) + if "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) + ] + model.load_state_dict(average_checkpoints(filenames)) + else: + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + # save checkpoints + filename = f"{params.exp_dir}/epoch-{params.epoch}-avg-{params.avg}.pt" + torch.save(model.state_dict(), filename) + else: + checkpoint = torch.load( + f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu" + ) + if "model" not in checkpoint: + model.load_state_dict(checkpoint, strict=True) + else: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + 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) + multi_dataset = 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 + + test_sets_cuts = multi_dataset.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, + ) + + 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() diff --git a/egs/speech_llm/ASR_LLM/whisper_llm_zh/ds_config_zero1.json b/egs/speech_llm/ASR_LLM/whisper_llm_zh/ds_config_zero1.json new file mode 120000 index 000000000..af7162d6c --- /dev/null +++ b/egs/speech_llm/ASR_LLM/whisper_llm_zh/ds_config_zero1.json @@ -0,0 +1 @@ +../../../aishell/ASR/whisper/ds_config_zero1.json \ No newline at end of file diff --git a/egs/speech_llm/ASR_LLM/whisper_llm_zh/label_smoothing.py b/egs/speech_llm/ASR_LLM/whisper_llm_zh/label_smoothing.py new file mode 120000 index 000000000..e9d239fff --- /dev/null +++ b/egs/speech_llm/ASR_LLM/whisper_llm_zh/label_smoothing.py @@ -0,0 +1 @@ +../../../librispeech/ASR/conformer_ctc/label_smoothing.py \ No newline at end of file diff --git a/egs/speech_llm/ASR_LLM/whisper_llm_zh/model.py b/egs/speech_llm/ASR_LLM/whisper_llm_zh/model.py new file mode 100644 index 000000000..e2b9d7ecc --- /dev/null +++ b/egs/speech_llm/ASR_LLM/whisper_llm_zh/model.py @@ -0,0 +1,145 @@ +from torch import nn +import torch + +DEFAULT_SPEECH_TOKEN = -1997 # "" + +class EncoderProjector(nn.Module): + + def __init__(self, encoder_dim, llm_dim): + super().__init__() + self.linear1 = nn.Linear(encoder_dim, llm_dim) + self.relu = nn.ReLU() + self.linear2 = nn.Linear(llm_dim, llm_dim) + + def forward(self, x): + x = self.linear1(x) + x = self.relu(x) + x = self.linear2(x) + return x + +class SPEECH_LLM(nn.Module): + # https://github.com/ddlBoJack/SLAM-LLM/blob/main/src/slam_llm/models/slam_model.py + def __init__( + self, + encoder: nn.Module, + llm: nn.Module, + encoder_projector: nn.Module, + ): + super().__init__() + + self.encoder = encoder + self.encoder.eval() + self.llm = llm + self.llm.eval() + self.encoder_projector = encoder_projector + self.encoder_outputs_downsample_rate = 4 + + def _merge_input_ids_with_speech_features(self, speech_features, inputs_embeds, input_ids, attention_mask, labels): + num_speechs, speech_len, embed_dim = speech_features.shape + batch_size, sequence_length = input_ids.shape + left_padding = not torch.sum(input_ids[:, -1] == torch.tensor(self.llm.config.pad_token_id)) + # 1. Create a mask to know where special speech tokens are + special_speech_token_mask = input_ids == DEFAULT_SPEECH_TOKEN + num_special_speech_tokens = torch.sum(special_speech_token_mask, dim=-1) + # Compute the maximum embed dimension + max_embed_dim = (num_special_speech_tokens.max() * (speech_len - 1)) + sequence_length + batch_indices, non_speech_indices = torch.where(input_ids != DEFAULT_SPEECH_TOKEN) + + # 2. Compute the positions where text should be written + # Calculate new positions for text tokens in merged speech-text sequence. + # `special_speech_token_mask` identifies speech tokens. Each speech token will be replaced by `nb_text_tokens_per_speechs - 1` text tokens. + # `torch.cumsum` computes how each speech token shifts subsequent text token positions. + # - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one. + new_token_positions = torch.cumsum((special_speech_token_mask * (speech_len - 1) + 1), -1) - 1 + nb_speech_pad = max_embed_dim - 1 - new_token_positions[:, -1] + if left_padding: + new_token_positions += nb_speech_pad[:, None] # offset for left padding + text_to_overwrite = new_token_positions[batch_indices, non_speech_indices] + + # 3. Create the full embedding, already padded to the maximum position + final_embedding = torch.zeros( + batch_size, max_embed_dim, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device + ) + final_attention_mask = torch.zeros( + batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device + ) + if labels is not None: + final_labels = torch.full( + (batch_size, max_embed_dim), self., dtype=input_ids.dtype, device=input_ids.device + ) + # In case the Vision model or the Language model has been offloaded to CPU, we need to manually + # set the corresponding tensors into their correct target device. + target_device = inputs_embeds.device + batch_indices, non_speech_indices, text_to_overwrite = ( + batch_indices.to(target_device), + non_speech_indices.to(target_device), + text_to_overwrite.to(target_device), + ) + attention_mask = attention_mask.to(target_device) + + # 4. Fill the embeddings based on the mask. If we have ["hey" "", "how", "are"] + # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the speech features + final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_speech_indices] + final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_speech_indices] + if labels is not None: + final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_speech_indices] + + # 5. Fill the embeddings corresponding to the speechs. Anything that is not `text_positions` needs filling (#29835) + speech_to_overwrite = torch.full( + (batch_size, max_embed_dim), True, dtype=torch.bool, device=inputs_embeds.device + ) + speech_to_overwrite[batch_indices, text_to_overwrite] = False + speech_to_overwrite &= speech_to_overwrite.cumsum(-1) - 1 >= nb_speech_pad[:, None].to(target_device) + + if speech_to_overwrite.sum() != speech_features.shape[:-1].numel(): + raise ValueError( + f"The input provided to the model are wrong. The number of speech tokens is {torch.sum(special_speech_token_mask)} while" + f" the number of speech given to the model is {num_speechs}. This prevents correct indexing and breaks batch generation." + ) + + final_embedding[speech_to_overwrite] = speech_features.contiguous().reshape(-1, embed_dim).to(target_device) + final_attention_mask |= speech_to_overwrite + position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1) + + # 6. Mask out the embedding at padding positions, as we later use the past_key_value value to determine the non-attended tokens. + batch_indices, pad_indices = torch.where(input_ids == self.llm.config.pad_token_id) + indices_to_mask = new_token_positions[batch_indices, pad_indices] + + final_embedding[batch_indices, indices_to_mask] = 0 + + if labels is None: + final_labels = None + + return final_embedding, final_attention_mask, final_labels, position_ids + + def forward(self, + fbank: torch.Tensor = None, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor = None, + labels: torch.LongTensor = None, + ): + encoder_outs = self.encoder(fbank) + # downsample encoder_outs by 4 + encoder_outs = encoder_outs[:, ::self.encoder_outputs_downsample_rate] + speech_features = self.encoder_projector(encoder_outs) + + inputs_embeds = self.llm.get_input_embeddings()(input_ids) + inputs_embeds, attention_mask, labels, position_ids = self._merge_input_ids_with_speech_features( + speech_features, inputs_embeds, input_ids, attention_mask, labels + ) + + outputs = self.language_model( + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + logits = outputs[0] + + model_outputs = self.llm(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels, position_ids=position_ids) + + return model_outputs diff --git a/egs/speech_llm/ASR_LLM/whisper_llm_zh/multi_dataset.py b/egs/speech_llm/ASR_LLM/whisper_llm_zh/multi_dataset.py new file mode 100644 index 000000000..ad48c6bf0 --- /dev/null +++ b/egs/speech_llm/ASR_LLM/whisper_llm_zh/multi_dataset.py @@ -0,0 +1,258 @@ +# Copyright 2023 Xiaomi Corp. (authors: Zengrui Jin) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import glob +import logging +import re +from pathlib import Path +from typing import Dict, List + +import lhotse +from lhotse import CutSet, load_manifest_lazy + + +class MultiDataset: + def __init__(self, fbank_dir: str): + """ + Args: + manifest_dir: + It is expected to contain the following files: + - aishell_cuts_train.jsonl.gz + - aishell2_cuts_train.jsonl.gz + - aishell4_cuts_train_L.jsonl.gz + - aishell4_cuts_train_M.jsonl.gz + - aishell4_cuts_train_S.jsonl.gz + - alimeeting-far_cuts_train.jsonl.gz + - magicdata_cuts_train.jsonl.gz + - primewords_cuts_train.jsonl.gz + - stcmds_cuts_train.jsonl.gz + - thchs_30_cuts_train.jsonl.gz + - kespeech/kespeech-asr_cuts_train_phase1.jsonl.gz + - kespeech/kespeech-asr_cuts_train_phase2.jsonl.gz + - wenetspeech/cuts_L_fixed.jsonl.gz + """ + self.fbank_dir = Path(fbank_dir) + + def train_cuts(self) -> CutSet: + logging.info("About to get multidataset train cuts") + + # THCHS-30 + logging.info("Loading THCHS-30 in lazy mode") + thchs_30_cuts = load_manifest_lazy( + self.fbank_dir / "thchs_30_cuts_train.jsonl.gz" + ) + + # AISHELL-1 + logging.info("Loading Aishell-1 in lazy mode") + aishell_cuts = load_manifest_lazy( + self.fbank_dir / "aishell_cuts_train.jsonl.gz" + ) + + # AISHELL-2 + logging.info("Loading Aishell-2 in lazy mode") + aishell_2_cuts = load_manifest_lazy( + self.fbank_dir / "aishell2_cuts_train.jsonl.gz" + ) + + # AISHELL-4 + logging.info("Loading Aishell-4 in lazy mode") + aishell_4_L_cuts = load_manifest_lazy( + self.fbank_dir / "aishell4_cuts_train_L.jsonl.gz" + ) + aishell_4_M_cuts = load_manifest_lazy( + self.fbank_dir / "aishell4_cuts_train_M.jsonl.gz" + ) + aishell_4_S_cuts = load_manifest_lazy( + self.fbank_dir / "aishell4_cuts_train_S.jsonl.gz" + ) + + # ST-CMDS + logging.info("Loading ST-CMDS in lazy mode") + stcmds_cuts = load_manifest_lazy(self.fbank_dir / "stcmds_cuts_train.jsonl.gz") + + # Primewords + logging.info("Loading Primewords in lazy mode") + primewords_cuts = load_manifest_lazy( + self.fbank_dir / "primewords_cuts_train.jsonl.gz" + ) + + # MagicData + logging.info("Loading MagicData in lazy mode") + magicdata_cuts = load_manifest_lazy( + self.fbank_dir / "magicdata_cuts_train.jsonl.gz" + ) + + # Ali-Meeting + logging.info("Loading Ali-Meeting in lazy mode") + alimeeting_cuts = load_manifest_lazy( + self.fbank_dir / "alimeeting-far_cuts_train.jsonl.gz" + ) + + # WeNetSpeech + logging.info("Loading WeNetSpeech in lazy mode") + wenetspeech_L_cuts = load_manifest_lazy( + self.fbank_dir / "wenetspeech" / "cuts_L_fixed.jsonl.gz" + ) + + # KeSpeech + logging.info("Loading KeSpeech in lazy mode") + kespeech_1_cuts = load_manifest_lazy( + self.fbank_dir / "kespeech" / "kespeech-asr_cuts_train_phase1.jsonl.gz" + ) + kespeech_2_cuts = load_manifest_lazy( + self.fbank_dir / "kespeech" / "kespeech-asr_cuts_train_phase2.jsonl.gz" + ) + + return CutSet.mux( + thchs_30_cuts, + aishell_cuts, + aishell_2_cuts, + aishell_4_L_cuts, + aishell_4_M_cuts, + aishell_4_S_cuts, + alimeeting_cuts, + stcmds_cuts, + primewords_cuts, + magicdata_cuts, + wenetspeech_L_cuts, + kespeech_1_cuts, + kespeech_2_cuts, + weights=[ + len(thchs_30_cuts), + len(aishell_cuts), + len(aishell_2_cuts), + len(aishell_4_L_cuts), + len(aishell_4_M_cuts), + len(aishell_4_S_cuts), + len(alimeeting_cuts), + len(stcmds_cuts), + len(primewords_cuts), + len(magicdata_cuts), + len(wenetspeech_L_cuts), + len(kespeech_1_cuts), + len(kespeech_2_cuts), + ], + ) + + def dev_cuts(self) -> CutSet: + logging.info("About to get multidataset dev cuts") + + # WeNetSpeech + logging.info("Loading WeNetSpeech DEV set in lazy mode") + wenetspeech_dev_cuts = load_manifest_lazy( + self.fbank_dir / "wenetspeech" / "cuts_DEV_fixed.jsonl.gz" + ) + + return wenetspeech_dev_cuts + + def test_cuts(self) -> Dict[str, CutSet]: + logging.info("About to get multidataset test cuts") + + # AISHELL + logging.info("Loading Aishell set in lazy mode") + aishell_test_cuts = load_manifest_lazy( + self.fbank_dir / "aishell_cuts_test.jsonl.gz" + ) + aishell_dev_cuts = load_manifest_lazy( + self.fbank_dir / "aishell_cuts_dev.jsonl.gz" + ) + + # AISHELL-2 + logging.info("Loading Aishell-2 set in lazy mode") + aishell2_test_cuts = load_manifest_lazy( + self.fbank_dir / "aishell2_cuts_test.jsonl.gz" + ) + aishell2_dev_cuts = load_manifest_lazy( + self.fbank_dir / "aishell2_cuts_dev.jsonl.gz" + ) + + # AISHELL-4 + logging.info("Loading Aishell-4 TEST set in lazy mode") + aishell4_test_cuts = load_manifest_lazy( + self.fbank_dir / "aishell4_cuts_test.jsonl.gz" + ) + + # Ali-Meeting + logging.info("Loading Ali-Meeting set in lazy mode") + alimeeting_test_cuts = load_manifest_lazy( + self.fbank_dir / "alimeeting-far_cuts_test.jsonl.gz" + ) + alimeeting_eval_cuts = load_manifest_lazy( + self.fbank_dir / "alimeeting-far_cuts_eval.jsonl.gz" + ) + + # MagicData + logging.info("Loading MagicData set in lazy mode") + magicdata_test_cuts = load_manifest_lazy( + self.fbank_dir / "magicdata_cuts_test.jsonl.gz" + ) + magicdata_dev_cuts = load_manifest_lazy( + self.fbank_dir / "magicdata_cuts_dev.jsonl.gz" + ) + + # KeSpeech + logging.info("Loading KeSpeech set in lazy mode") + kespeech_test_cuts = load_manifest_lazy( + self.fbank_dir / "kespeech" / "kespeech-asr_cuts_test.jsonl.gz" + ) + kespeech_dev_phase1_cuts = load_manifest_lazy( + self.fbank_dir / "kespeech" / "kespeech-asr_cuts_dev_phase1.jsonl.gz" + ) + kespeech_dev_phase2_cuts = load_manifest_lazy( + self.fbank_dir / "kespeech" / "kespeech-asr_cuts_dev_phase2.jsonl.gz" + ) + + # WeNetSpeech + logging.info("Loading WeNetSpeech set in lazy mode") + wenetspeech_test_meeting_cuts = load_manifest_lazy( + self.fbank_dir / "wenetspeech" / "cuts_TEST_MEETING.jsonl.gz" + ) + wenetspeech_test_net_cuts = load_manifest_lazy( + self.fbank_dir / "wenetspeech" / "cuts_TEST_NET.jsonl.gz" + ) + wenetspeech_dev_cuts = load_manifest_lazy( + self.fbank_dir / "wenetspeech" / "cuts_DEV_fixed.jsonl.gz" + ) + + return { + "wenetspeech-meeting_test": wenetspeech_test_meeting_cuts, + # "aishell_test": aishell_test_cuts, + # "aishell_dev": aishell_dev_cuts, + # "ali-meeting_test": alimeeting_test_cuts, + # "ali-meeting_eval": alimeeting_eval_cuts, + # "aishell-4_test": aishell4_test_cuts, + # "aishell-2_test": aishell2_test_cuts, + # "aishell-2_dev": aishell2_dev_cuts, + # "magicdata_test": magicdata_test_cuts, + # "magicdata_dev": magicdata_dev_cuts, + # "kespeech-asr_test": kespeech_test_cuts, + # "kespeech-asr_dev_phase1": kespeech_dev_phase1_cuts, + # "kespeech-asr_dev_phase2": kespeech_dev_phase2_cuts, + # "wenetspeech-net_test": wenetspeech_test_net_cuts, + # "wenetspeech_dev": wenetspeech_dev_cuts, + } + + def aishell_train_cuts(self) -> CutSet: + logging.info("About to get multidataset train cuts") + + # AISHELL-1 + logging.info("Loading Aishell-1 in lazy mode") + aishell_cuts = load_manifest_lazy( + self.fbank_dir / "aishell_cuts_train.jsonl.gz" + ) + + return aishell_cuts \ No newline at end of file diff --git a/egs/speech_llm/ASR_LLM/whisper_llm_zh/train.py b/egs/speech_llm/ASR_LLM/whisper_llm_zh/train.py new file mode 100755 index 000000000..720b2c6ff --- /dev/null +++ b/egs/speech_llm/ASR_LLM/whisper_llm_zh/train.py @@ -0,0 +1,874 @@ +#!/usr/bin/env python3 +# Copyright 2023 Xiaomi Corp. (authors: Xiaoyu Yang) +# 2024 Yuekai Zhang +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +#fine-tuning with deepspeed zero stage 1 +torchrun --nproc-per-node 8 ./whisper/train.py \ + --max-duration 200 \ + --exp-dir whisper/exp_large_v2 \ + --model-name large-v2 \ + --deepspeed \ + --deepspeed_config ./whisper/ds_config_zero1.json +""" + +import argparse +import copy +import logging +import os +import random +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, List, Optional, Tuple, Union + +import deepspeed +import k2 +import optim +import torch +import torch.multiprocessing as mp +import torch.nn as nn +import whisper +from asr_datamodule import AsrDataModule +from model import SPEECH_LLM, EncoderProjector +from deepspeed.utils.zero_to_fp32 import convert_zero_checkpoint_to_fp32_state_dict +from label_smoothing import LabelSmoothingLoss +from lhotse import CutSet, load_manifest +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from multi_dataset import MultiDataset +# from optim import Eden, ScaledAdam +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.functional import pad as pad_tensor +# from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter +from whisper_decoder_forward_monkey_patch import replace_whisper_decoder_forward +from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward + +from icefall import diagnostics +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import update_averaged_model +from icefall.dist import cleanup_dist, get_rank, get_world_size, setup_dist +from icefall.env import get_env_info +from icefall.hooks import register_inf_check_hooks +from icefall.utils import ( + AttributeDict, + MetricsTracker, + filter_uneven_sized_batch, + setup_logger, + str2bool, +) + +from transformers import AutoModelForCausalLM, AutoTokenizer +LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler] + + +def set_batch_count(model: Union[nn.Module, DDP], batch_count: float) -> None: + if isinstance(model, DDP): + # get underlying nn.Module + model = model.module + for module in model.modules(): + if hasattr(module, "batch_count"): + module.batch_count = batch_count + +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--llm-path-or-name", + type=str, + default="/workspace/asr/Qwen1.5-0.5B-Chat", + help="Path or name of the large language model.", + ) + + parser.add_argument( + "--speech-encoder-path-or-name", + type=str, + default="whisper-large-v2", + help="Path or name of the speech encoder.", + ) + + parser.add_argument( + "--encoder-projector-ds-rate", + type=int, + default=4, + help="Downsample rate for the encoder projector.", + ) + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=10, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=1, + help="""Resume training from this epoch. It should be positive. + If larger than 1, it will load checkpoint from + exp-dir/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="whisper_qwen/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--pretrained-model-path", + type=str, + default=None, + help="""The path to the pretrained model if it is not None. Training will + start from this model. e.g. ./wenetspeech/ASR/whisper/exp_large_v2/epoch-4-avg-3.pt + """, + ) + + parser.add_argument( + "--base-lr", type=float, default=1e-5, help="The base learning rate." + ) + + parser.add_argument( + "--lr-batches", + type=float, + default=5000, + help="""Number of steps that affects how rapidly the learning rate + decreases. We suggest not to change this.""", + ) + + parser.add_argument( + "--lr-epochs", + type=float, + default=6, + help="""Number of epochs that affects how rapidly the learning rate decreases. + """, + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--print-diagnostics", + type=str2bool, + default=False, + help="Accumulate stats on activations, print them and exit.", + ) + + parser.add_argument( + "--inf-check", + type=str2bool, + default=False, + help="Add hooks to check for infinite module outputs and gradients.", + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=30, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + parser.add_argument( + "--average-period", + type=int, + default=200, + help="""Update the averaged model, namely `model_avg`, after processing + this number of batches. `model_avg` is a separate version of model, + in which each floating-point parameter is the average of all the + parameters from the start of training. Each time we take the average, + we do: `model_avg = model * (average_period / batch_idx_train) + + model_avg * ((batch_idx_train - average_period) / batch_idx_train)`. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=True, + help="Whether to use half precision training.", + ) + + parser = deepspeed.add_config_arguments(parser) + add_model_arguments(parser) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - frame_shift_ms: The frame shift in milliseconds. + - allowed_excess_duration_ratio: The allowed excess duration ratio. + - best_train_loss: The best training loss so far. + - best_valid_loss: The best validation loss so far. + - best_train_epoch: The epoch where the best training loss is achieved. + - best_valid_epoch: The epoch where the best validation loss is achieved. + - batch_idx_train: The batch index of the current batch. + - log_interval: Log training stats every `log_interval` batches. + - reset_interval: Reset the stats every `reset_interval` batches. + - valid_interval: Run validation every `valid_interval` batches. + - env_info: The environment information. + """ + params = AttributeDict( + { + "allowed_excess_duration_ratio": 0.1, + "frame_shift_ms": 10, + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 200, + "valid_interval": 10000, + "env_info": get_env_info(), + } + ) + + return params + + +# def load_checkpoint_if_available( +# params: AttributeDict, +# model: nn.Module, +# model_avg: nn.Module = None, +# optimizer: Optional[torch.optim.Optimizer] = None, +# scheduler: Optional[LRSchedulerType] = None, +# ) -> Optional[Dict[str, Any]]: +# """Load checkpoint from file. + +# If params.start_batch is positive, it will load the checkpoint from +# `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if +# params.start_epoch is larger than 1, it will load the checkpoint from +# `params.start_epoch - 1`. + +# Apart from loading state dict for `model` and `optimizer` it also updates +# `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, +# and `best_valid_loss` in `params`. + +# Args: +# params: +# The return value of :func:`get_params`. +# model: +# The training model. +# model_avg: +# The stored model averaged from the start of training. +# optimizer: +# The optimizer that we are using. +# scheduler: +# The scheduler that we are using. +# Returns: +# Return a dict containing previously saved training info. +# """ +# if params.start_batch > 0: +# filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" +# elif params.start_epoch > 1: +# filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" +# else: +# return None + +# assert filename.is_file(), f"{filename} does not exist!" + +# saved_params = load_checkpoint( +# filename, +# model=model, +# model_avg=model_avg, +# optimizer=optimizer, +# scheduler=scheduler, +# ) + +# keys = [ +# "best_train_epoch", +# "best_valid_epoch", +# "batch_idx_train", +# "best_train_loss", +# "best_valid_loss", +# ] +# for k in keys: +# params[k] = saved_params[k] + +# if params.start_batch > 0: +# if "cur_epoch" in saved_params: +# params["start_epoch"] = saved_params["cur_epoch"] + +# return saved_params + + +def compute_loss( + params: AttributeDict, + tokenizer: AutoTokenizer, + model: Union[nn.Module, DDP], + batch: dict, + is_training: bool, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute the loss for the given batch. + Args: + params: + It is returned by :func:`get_params`. + tokenizer: + The tokenizer used to encode the text. + model: + The model for training. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + Whether it is training. + Returns: + Return a tuple of two elements. The first element is the loss tensor. + """ + # For the uneven-sized batch, the total duration after padding would possibly + # cause OOM. Hence, for each batch, which is sorted descendingly by length, + # we simply drop the last few shortest samples, so that the retained total frames + # (after padding) would not exceed `allowed_max_frames`: + # `allowed_max_frames = int(max_frames * (1.0 + allowed_excess_duration_ratio))`, + # where `max_frames = max_duration * 1000 // frame_shift_ms`. + # We set allowed_excess_duration_ratio=0.1. + + def preprocess( + messages, + tokenizer: transformers.PreTrainedTokenizer, + max_len: int, + ) -> Dict: + """Preprocesses the data for supervised fine-tuning.""" + texts = [] + for i, msg in enumerate(messages): + texts.append( + tokenizer.apply_chat_template( + msg, + chat_template=TEMPLATE, + tokenize=True, + add_generation_prompt=False, + padding="max_length", + max_length=max_len, + truncation=True, + ) + ) + # model_inputs = tokenizer([text], return_tensors="pt").to(device) + input_ids = torch.tensor(texts, dtype=torch.int) + target_ids = input_ids.clone() + target_ids[target_ids == tokenizer.pad_token_id] = IGNORE_TOKEN_ID + attention_mask = input_ids.ne(tokenizer.pad_token_id) + + 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("", "") + text = text.replace("<%>", "") + text = text.replace("<->", "") + text = text.replace("<$>", "") + text = text.replace("<#>", "") + text = text.replace("<_>", "") + text = text.replace("", "") + 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) + + device = model.device if isinstance(model, DDP) else next(model.parameters()).device + feature = batch["inputs"] + + assert feature.ndim == 3 + feature = feature.to(device) + feature = feature.transpose(1, 2) # (N, C, T) + + # feature_lens = supervisions["num_frames"].to(device) + + batch_idx_train = params.batch_idx_train + supervisions = batch["supervisions"] + texts = batch["supervisions"]["text"] + # remove spaces in texts + texts = [normalize_text_alimeeting(text) for text in texts] + + # messages = [ + # {"role": "system", "content": "You are a helpful assistant."}, + # {"role": "user", "content": prompt} + # ] + input_ids, attention_mask, target_ids = preprocess( + texts, tokenizer, max_len=512 + ) + + # decoder_criterion = LabelSmoothingLoss( + # ignore_index=50256, label_smoothing=0.1, reduction="sum" + # ) + + # # ignore the first 3 tokens, which are always <|lang_id|>, <|transcibe|>, <|notimestampes|> + # ignore_prefix_size = 3 + # with torch.set_grad_enabled(is_training): + # encoder_out = model.encoder(feature) + # text_logits = model.decoder(prev_outputs_tokens.to(device), encoder_out) + # text_logits = text_logits[:, ignore_prefix_size:, :] + # target_tokens = target_tokens[:, ignore_prefix_size:] + # loss = decoder_criterion(text_logits, target_tokens.to(device)) + + # assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = (feature_lens // params.subsampling_factor).sum().item() + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + tokenizer: whisper.tokenizer.Tokenizer, + model: Union[nn.Module, DDP], + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + tokenizer=tokenizer, + model=model, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + tokenizer: AutoTokenizer, + model: Union[nn.Module, DDP], + optimizer: torch.optim.Optimizer, + scheduler: LRSchedulerType, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + scheduler: + The learning rate scheduler, we call step() every step. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + model_avg: + The stored model averaged from the start of training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.encoder_projector.train() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(train_dl): + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + if batch_idx % params.valid_interval == 0 and not params.print_diagnostics: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + tokenizer=tokenizer, + model=model, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + logging.info( + f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB" + ) + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + model.save_checkpoint( + save_dir=params.exp_dir, + tag=f"epoch-{params.cur_epoch}-checkpoint-{batch_idx}", + client_state={"sampler": train_dl.sampler.state_dict()}, + ) + if rank == 0: + convert_zero_checkpoint_to_fp32_state_dict( + params.exp_dir, + f"{params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}.pt", + tag=f"epoch-{params.cur_epoch}-checkpoint-{batch_idx}", + ) + os.system( + f"rm -rf {params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}" + ) + + try: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + tokenizer=tokenizer, + model=model, + batch=batch, + is_training=True, + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + + # deepspeed's backward() is different from torch's backward() + # in that it does not accept a loss tensor as input. + # It computes the loss internally. + model.backward(loss) + model.step() + + except: # noqa + display_and_save_batch(batch, params=params) + raise + + if batch_idx % params.log_interval == 0: + try: + cur_lr = scheduler.get_last_lr()[0] + except: # noqa + cur_lr = 0.0 + + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}, " + f"lr: {cur_lr:.2e}, " + ) + + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + + fix_random_seed(params.seed) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info(params) + + logging.info("About to create model") + + if 'whisper' in params.speech_encoder_path_or_name: + replace_whisper_encoder_forward() + # TODO: directly loading from whisper-ft checkpoint + # whisper-large-v2-multi-hans-zh-epoch-3-avg-10.pt + speech_encoder = whisper.load_model(params.model_name, "cpu").encoder + speech_encoder_dim = speech_encoder.dims.n_audio_ctx + + llm = AutoModelForCausalLM.from_pretrained( + params.llm_path_or_name, + attn_implemented="flash_attention_2", + device_map="cpu" + ) + tokenizer = AutoTokenizer.from_pretrained(params.llm_path_or_name) + + encoder_projector = EncoderProjector(speech_encoder_dim, llm.config.hidden_size) + + model = SPEECH_LLM( + speech_encoder, + llm, + encoder_projector, + ) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + else: + device = torch.device("cpu") + logging.info(f"Device: {device}") + model.to(device) + + # assert params.start_epoch > 0, params.start_epoch + # checkpoints = load_checkpoint_if_available( + # params=params, model=model, model_avg=model_avg + # ) + + assert params.deepspeed and world_size > 1 + logging.info("Using DeepSpeed") + model, optimizer, _, scheduler = deepspeed.initialize( + args=params, model=model, model_parameters=model.parameters() + ) + + data_module = AsrDataModule(args) + multi_dataset = MultiDataset(args.manifest_dir) + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + if c.duration < 1.0 or c.duration > 20.0: + logging.warning( + f"Exclude cut with ID {c.id} from training. Duration: {c.duration}" + ) + return False + return True + + # train_cuts = multi_dataset.train_cuts() + train_cuts = multi_dataset.aishell_train_cuts() + train_cuts = train_cuts.filter(remove_short_and_long_utt) + + # if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: + # # We only load the sampler's state dict when it loads a checkpoint + # # saved in the middle of an epoch + # sampler_state_dict = checkpoints["sampler"] + # else: + # sampler_state_dict = None + # TODO: load sampler state dict + train_dl = data_module.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + valid_cuts = multi_dataset.dev_cuts() + valid_dl = data_module.valid_dataloaders(valid_cuts) + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + # if params.pretrained_model_path: + # checkpoint = torch.load(params.pretrained_model_path, map_location="cpu") + # if "model" not in checkpoint: + # model.load_state_dict(checkpoint, strict=True) + # else: + # load_checkpoint(params.pretrained_model_path, model) + + logging.info(f"start training from epoch {params.start_epoch}") + for epoch in range(params.start_epoch, params.num_epochs + 1): + + fix_random_seed(params.seed + epoch - 1) + train_dl.sampler.set_epoch(epoch - 1) + + if tb_writer is not None: + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + tokenizer=tokenizer, + model=model, + optimizer=optimizer, + scheduler=scheduler, + train_dl=train_dl, + valid_dl=valid_dl, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + + model.save_checkpoint( + save_dir=params.exp_dir, + tag=f"epoch-{params.cur_epoch}", + client_state={"sampler": train_dl.sampler.state_dict()}, + ) + if rank == 0: + convert_zero_checkpoint_to_fp32_state_dict( + params.exp_dir, + f"{params.exp_dir}/epoch-{params.cur_epoch}.pt", + tag=f"epoch-{params.cur_epoch}", + ) + os.system(f"rm -rf {params.exp_dir}/epoch-{params.cur_epoch}") + + + logging.info("Done!") + +def display_and_save_batch( + batch: dict, + params: AttributeDict, +) -> None: + """Display the batch statistics and save the batch into disk. + + Args: + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + params: + Parameters for training. See :func:`get_params`. + """ + from lhotse.utils import uuid4 + + filename = f"{params.exp_dir}/batch-{uuid4()}.pt" + logging.info(f"Saving batch to {filename}") + torch.save(batch, filename) + + supervisions = batch["supervisions"] + features = batch["inputs"] + + logging.info(f"features shape: {features.shape}") + + +def main(): + parser = get_parser() + AsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + world_size = get_world_size() + rank = get_rank() + + torch.set_num_threads(1) + torch.set_num_interop_threads(1) + run(rank=rank, world_size=world_size, args=args) + + +if __name__ == "__main__": + main() diff --git a/egs/speech_llm/ASR_LLM/whisper_llm_zh/whisper_encoder_forward_monkey_patch.py b/egs/speech_llm/ASR_LLM/whisper_llm_zh/whisper_encoder_forward_monkey_patch.py new file mode 120000 index 000000000..2a7808921 --- /dev/null +++ b/egs/speech_llm/ASR_LLM/whisper_llm_zh/whisper_encoder_forward_monkey_patch.py @@ -0,0 +1 @@ +../../../aishell/ASR/whisper/whisper_encoder_forward_monkey_patch.py \ No newline at end of file