([简体中文](./README_zh.md)|English) # Timestamp Prediction (FA) ## Inference ### Quick start #### [Use TP-Aligner Model Simply](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) ```python from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks inference_pipeline = pipeline( task=Tasks.speech_timestamp, model='damo/speech_timestamp_prediction-v1-16k-offline', model_revision='v1.1.0') rec_result = inference_pipeline( audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_timestamps.wav', text_in='一 个 东 太 平 洋 国 家 为 什 么 跑 到 西 太 平 洋 来 了 呢',) print(rec_result) ``` Timestamp pipeline can also be used after ASR pipeline to compose complete ASR function, ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/246). ### API-reference #### Define pipeline - `task`: `Tasks.speech_timestamp` - `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk - `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU - `ncpu`: `1` (Default), sets the number of threads used for intraop parallelism on CPU - `output_dir`: `None` (Default), the output path of results if set - `batch_size`: `1` (Default), batch size when decoding #### Infer pipeline - `audio_in`: the input speech to predict, which could be: - wav_path, `e.g.`: asr_example.wav (wav in local or url), - wav.scp, kaldi style wav list (`wav_id wav_path`), `e.g.`: ```text asr_example1 ./audios/asr_example1.wav asr_example2 ./audios/asr_example2.wav ``` In this case of `wav.scp` input, `output_dir` must be set to save the output results - `text_in`: the input text to predict， splited by blank, which could be: - text string, `e.g.`: `今 天 天 气 怎 么 样` - text.scp, kaldi style text file (`wav_id transcription`), `e.g.`: ```text asr_example1 今 天 天 气 怎 么 样 asr_example2 欢 迎 体 验 达 摩 院 语 音 识 别 模 型 ``` - `audio_fs`: audio sampling rate, only set when audio_in is pcm audio - `output_dir`: None (Default), the output path of results if set, containing - output_dir/timestamp_prediction/tp_sync, timestamp in second containing silence periods, `wav_id# token1 start_time end_time;`, `e.g.`: ```text test_wav1# 0.000 0.500;温 0.500 0.680;州 0.680 0.840;化 0.840 1.040;工 1.040 1.280;仓 1.280 1.520; 1.520 1.680;库 1.680 1.920; 1.920 2.160;起 2.160 2.380;火 2.380 2.580;殃 2.580 2.760;及 2.760 2.920;附 2.920 3.100;近 3.100 3.340; 3.340 3.400;河 3.400 3.640; 3.640 3.700;流 3.700 3.940; 3.940 4.240;大 4.240 4.400;量 4.400 4.520;死 4.520 4.680;鱼 4.680 4.920; 4.920 4.940;漂 4.940 5.120;浮 5.120 5.300;河 5.300 5.500;面 5.500 5.900; 5.900 6.240; ``` - output_dir/timestamp_prediction/tp_time, timestamp list in ms of same length as input text without silence `wav_id# [[start_time, end_time],]`, `e.g.`: ```text test_wav1# [[500, 680], [680, 840], [840, 1040], [1040, 1280], [1280, 1520], [1680, 1920], [2160, 2380], [2380, 2580], [2580, 2760], [2760, 2920], [2920, 3100], [3100, 3340], [3400, 3640], [3700, 3940], [4240, 4400], [4400, 4520], [4520, 4680], [4680, 4920], [4940, 5120], [5120, 5300], [5300, 5500], [5500, 5900]] ``` ### Inference with multi-thread CPUs or multi GPUs FunASR also offer recipes [egs_modelscope/tp/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/tp/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs. #### Settings of `infer.sh` - `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk - `data_dir`: the dataset dir **must** include `wav.scp` and `text.txt` - `output_dir`: output dir of the recognition results - `batch_size`: `64` (Default), batch size of inference on gpu - `gpu_inference`: `true` (Default), whether to perform gpu decoding, set false for CPU inference - `gpuid_list`: `0,1` (Default), which gpu_ids are used to infer - `njob`: only used for CPU inference (`gpu_inference`=`false`), `64` (Default), the number of jobs for CPU decoding - `checkpoint_dir`: only used for infer finetuned models, the path dir of finetuned models - `checkpoint_name`: only used for infer finetuned models, `valid.cer_ctc.ave.pb` (Default), which checkpoint is used to infer #### Decode with multi GPUs: ```shell bash infer.sh \ --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \ --data_dir "./data/test" \ --output_dir "./results" \ --batch_size 1 \ --gpu_inference true \ --gpuid_list "0,1" ``` #### Decode with multi-thread CPUs: ```shell bash infer.sh \ --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \ --data_dir "./data/test" \ --output_dir "./results" \ --gpu_inference false \ --njob 1 ``` ## Finetune with pipeline ### Quick start ### Finetune with your data ## Inference with your finetuned model