(简体中文|English)
FunASR has open-sourced a large number of pre-trained models on industrial data. You are free to use, copy, modify, and share FunASR models under the Model License Agreement. Below, we list some representative models. For a comprehensive list, please refer to our Model Zoo.
For command-line invocation:
funasr ++model=paraformer-zh ++vad_model="fsmn-vad" ++punc_model="ct-punc" ++input=asr_example_zh.wavFor python code invocation (recommended):
from funasr import AutoModel
model = AutoModel(model="paraformer-zh")
res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav")
print(res)model = AutoModel(model=[str], device=[str], ncpu=[int], output_dir=[str], batch_size=[int], hub=[str], **kwargs)model(str): model name in the Model Repository, or a model path on local disk.device(str):cuda:0(default gpu0) for using GPU for inference, specifycpufor using CPU.ncpu(int):4(default), sets the number of threads for CPU internal operations.output_dir(str):None(default), set this to specify the output path for the results.batch_size(int):1(default), the number of samples per batch during decoding.hub(str)：ms(default) to download models from ModelScope. Usehfto download models from Hugging Face.**kwargs(dict): Any parameters found in config.yaml can be directly specified here, for instance, the maximum segmentation length in the vad model max_single_segment_time=6000 (milliseconds).
res = model.generate(input=[str], output_dir=[str])input: The input to be decoded, which could be:- A wav file path, e.g., asr_example.wav
- A pcm file path, e.g., asr_example.pcm, in this case, specify the audio sampling rate fs (default is 16000)
- An audio byte stream, e.g., byte data from a microphone
- A wav.scp, a Kaldi-style wav list (wav_id \t wav_path), for example:
When using wav.scp as input, you must set output_dir to save the output results.asr_example1 ./audios/asr_example1.wav asr_example2 ./audios/asr_example2.wav- Audio samples,
e.g.:audio, rate = soundfile.read("asr_example_zh.wav"), data type is numpy.ndarray. Supports batch inputs, type is list：[audio_sample1, audio_sample2, ..., audio_sampleN] - fbank input, supports batch grouping. Shape is [batch, frames, dim], type is torch.Tensor.
output_dir: None (default), if set, specifies the output path for the results.**kwargs(dict): Inference parameters related to the model, for example,beam_size=10，decoding_ctc_weight=0.1.
from funasr import AutoModel
# paraformer-zh is a multi-functional asr model
# use vad, punc, spk or not as you need
model = AutoModel(model="paraformer-zh",
vad_model="fsmn-vad",
vad_kwargs={"max_single_segment_time": 60000},
punc_model="ct-punc",
# spk_model="cam++"
)
wav_file = f"{model.model_path}/example/asr_example.wav"
res = model.generate(input=wav_file, batch_size_s=300, batch_size_threshold_s=60, hotword='魔搭')
print(res)Notes:
- Typically, the input duration for models is limited to under 30 seconds. However, when combined with
vad_model, support for audio input of any length is enabled, not limited to the paraformer model—any audio input model can be used. - Parameters related to model can be directly specified in the definition of AutoModel; parameters related to
vad_modelcan be set throughvad_kwargs, which is a dict; similar parameters includepunc_kwargsandspk_kwargs. max_single_segment_time: Denotes the maximum audio segmentation length forvad_model, measured in milliseconds (ms).batch_size_srepresents the use of dynamic batching, where the total audio duration within a batch is measured in seconds (s).batch_size_threshold_s: Indicates that when the duration of an audio segment post-VAD segmentation exceeds the batch_size_threshold_s threshold, the batch size is set to 1, measured in seconds (s).
Recommendations:
When you input long audio and encounter Out Of Memory (OOM) issues, since memory usage tends to increase quadratically with audio length, consider the following three scenarios:
a) At the beginning of inference, memory usage primarily depends on batch_size_s. Appropriately reducing this value can decrease memory usage.
b) During the middle of inference, when encountering long audio segments cut by VAD and the total token count is less than batch_size_s, yet still facing OOM, you can appropriately reduce batch_size_threshold_s. If the threshold is exceeded, the batch size is forced to 1.
c) Towards the end of inference, if long audio segments cut by VAD have a total token count less than batch_size_s and exceed the threshold batch_size_threshold_s, forcing the batch size to 1 and still facing OOM, you may reduce max_single_segment_time to shorten the VAD audio segment length.
from funasr import AutoModel
chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention
model = AutoModel(model="paraformer-zh-streaming")
import soundfile
import os
wav_file = os.path.join(model.model_path, "example/asr_example.wav")
speech, sample_rate = soundfile.read(wav_file)
chunk_stride = chunk_size[1] * 960 # 600ms
cache = {}
total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
print(res)Note: chunk_size is the configuration for streaming latency. [0,10,5] indicates that the real-time display granularity is 10*60=600ms, and the lookahead information is 5*60=300ms. Each inference input is 600ms (sample points are 16000*0.6=960), and the output is the corresponding text. For the last speech segment input, is_final=True needs to be set to force the output of the last word.
from funasr import AutoModel
model = AutoModel(model="fsmn-vad")
wav_file = f"{model.model_path}/example/vad_example.wav"
res = model.generate(input=wav_file)
print(res)Note: The output format of the VAD model is: [[beg1, end1], [beg2, end2], ..., [begN, endN]], where begN/endN indicates the starting/ending point of the N-th valid audio segment, measured in milliseconds.
from funasr import AutoModel
chunk_size = 200 # ms
model = AutoModel(model="fsmn-vad")
import soundfile
wav_file = f"{model.model_path}/example/vad_example.wav"
speech, sample_rate = soundfile.read(wav_file)
chunk_stride = int(chunk_size * sample_rate / 1000)
cache = {}
total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
if len(res[0]["value"]):
print(res)Note: The output format for the streaming VAD model can be one of four scenarios:
[[beg1, end1], [beg2, end2], .., [begN, endN]]：The same as the offline VAD output result mentioned above.[[beg, -1]]：Indicates that only a starting point has been detected.[[-1, end]]：Indicates that only an ending point has been detected.[]：Indicates that neither a starting point nor an ending point has been detected.
The output is measured in milliseconds and represents the absolute time from the starting point.
from funasr import AutoModel
model = AutoModel(model="ct-punc")
res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
print(res)from funasr import AutoModel
model = AutoModel(model="fa-zh")
wav_file = f"{model.model_path}/example/asr_example.wav"
text_file = f"{model.model_path}/example/text.txt"
res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
print(res)More examples ref to docs
Execute via command line (for quick testing, not recommended):
funasr-train ++model=paraformer-zh ++train_data_set_list=data/list/train.jsonl ++valid_data_set_list=data/list/val.jsonl ++output_dir="./outputs" &> log.txt &Execute with Python code (supports multi-node and multi-GPU, recommended):
cd examples/industrial_data_pretraining/paraformer
bash finetune.sh
# "log_file: ./outputs/log.txt"Full code ref to finetune.sh
funasr/bin/train.py \
++model="${model_name_or_model_dir}" \
++train_data_set_list="${train_data}" \
++valid_data_set_list="${val_data}" \
++dataset_conf.batch_size=20000 \
++dataset_conf.batch_type="token" \
++dataset_conf.num_workers=4 \
++train_conf.max_epoch=50 \
++train_conf.log_interval=1 \
++train_conf.resume=false \
++train_conf.validate_interval=2000 \
++train_conf.save_checkpoint_interval=2000 \
++train_conf.keep_nbest_models=20 \
++train_conf.avg_nbest_model=10 \
++optim_conf.lr=0.0002 \
++output_dir="${output_dir}" &> ${log_file}model（str）: The name of the model (the ID in the model repository), at which point the script will automatically download the model to local storage; alternatively, the path to a model already downloaded locally.train_data_set_list（str）: The path to the training data, typically in jsonl format, for specific details refer to examples.valid_data_set_list（str）：The path to the validation data, also generally in jsonl format, for specific details refer to examples](https://github.com/alibaba-damo-academy/FunASR/blob/main/data/list).dataset_conf.batch_type（str）：example (default), the type of batch. example means batches are formed with a fixed number of batch_size samples; length or token means dynamic batching, with total length or number of tokens of the batch equalling batch_size.dataset_conf.batch_size（int）：Used in conjunction with batch_type. When batch_type=example, it represents the number of samples; when batch_type=length, it represents the length of the samples, measured in fbank frames (1 frame = 10 ms) or the number of text tokens.train_conf.max_epoch（int）：The total number of epochs for training.train_conf.log_interval（int）：The number of steps between logging.train_conf.resume（int）：Whether to enable checkpoint resuming for training.train_conf.validate_interval（int）：The interval in steps to run validation tests during training.train_conf.save_checkpoint_interval（int）：The interval in steps for saving the model during training.train_conf.keep_nbest_models（int）：The maximum number of model parameters to retain, sorted by validation set accuracy, from highest to lowest.train_conf.avg_nbest_model（int）：Average over the top n models with the highest accuracy.optim_conf.lr（float）：The learning rate.output_dir（str）：The path for saving the model.**kwargs(dict): Any parameters in config.yaml can be specified directly here, for example, to filter out audio longer than 20s: dataset_conf.max_token_length=2000, measured in fbank frames (1 frame = 10 ms) or the number of text tokens.
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun --nnodes 1 --nproc_per_node ${gpu_num} \
../../../funasr/bin/train.py ${train_args}--nnodes represents the total number of participating nodes, while --nproc_per_node indicates the number of processes running on each node.
On the master node, assuming the IP is 192.168.1.1 and the port is 12345, and you're using 2 GPUs, you would run the following command:
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
../../../funasr/bin/train.py ${train_args}On the worker node (assuming the IP is 192.168.1.2), you need to ensure that the MASTER_ADDR and MASTER_PORT environment variables are set to match those of the master node, and then run the same command:
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
../../../funasr/bin/train.py ${train_args}--nnodes indicates the total number of nodes participating in the training, --node_rank represents the ID of the current node, and --nproc_per_node specifies the number of processes running on each node (usually corresponds to the number of GPUs).
jsonl ref to（demo）.
The instruction scp2jsonl can be used to generate from wav.scp and text.txt. The preparation process for wav.scp and text.txt is as follows:
train_text.txt
ID0012W0013 当客户风险承受能力评估依据发生变化时
ID0012W0014 所有只要处理 data 不管你是做 machine learning 做 deep learning
ID0012W0015 he tried to think how it could betrain_wav.scp
BAC009S0764W0121 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0764W0121.wav
BAC009S0916W0489 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0916W0489.wav
ID0012W0015 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_cn_en.wavCommand
# generate train.jsonl and val.jsonl from wav.scp and text.txt
scp2jsonl \
++scp_file_list='["../../../data/list/train_wav.scp", "../../../data/list/train_text.txt"]' \
++data_type_list='["source", "target"]' \
++jsonl_file_out="../../../data/list/train.jsonl"(Optional, not required) If you need to parse from jsonl back to wav.scp and text.txt, you can use the following command:
# generate wav.scp and text.txt from train.jsonl and val.jsonl
jsonl2scp \
++scp_file_list='["../../../data/list/train_wav.scp", "../../../data/list/train_text.txt"]' \
++data_type_list='["source", "target"]' \
++jsonl_file_in="../../../data/list/train.jsonl"tail log.txt
[2024-03-21 15:55:52,137][root][INFO] - train, rank: 3, epoch: 0/50, step: 6990/1, total step: 6990, (loss_avg_rank: 0.327), (loss_avg_epoch: 0.409), (ppl_avg_epoch: 1.506), (acc_avg_epoch: 0.795), (lr: 1.165e-04), [('loss_att', 0.259), ('acc', 0.825), ('loss_pre', 0.04), ('loss', 0.299), ('batch_size', 40)], {'data_load': '0.000', 'forward_time': '0.315', 'backward_time': '0.555', 'optim_time': '0.076', 'total_time': '0.947'}, GPU, memory: usage: 3.830 GB, peak: 18.357 GB, cache: 20.910 GB, cache_peak: 20.910 GB
[2024-03-21 15:55:52,139][root][INFO] - train, rank: 1, epoch: 0/50, step: 6990/1, total step: 6990, (loss_avg_rank: 0.334), (loss_avg_epoch: 0.409), (ppl_avg_epoch: 1.506), (acc_avg_epoch: 0.795), (lr: 1.165e-04), [('loss_att', 0.285), ('acc', 0.823), ('loss_pre', 0.046), ('loss', 0.331), ('batch_size', 36)], {'data_load': '0.000', 'forward_time': '0.334', 'backward_time': '0.536', 'optim_time': '0.077', 'total_time': '0.948'}, GPU, memory: usage: 3.943 GB, peak: 18.291 GB, cache: 19.619 GB, cache_peak: 19.619 GBrank：gpu id。epoch,step,total step：the current epoch, step, and total steps.loss_avg_rank：the average loss across all GPUs for the current step.loss/ppl/acc_avg_epoch：the overall average loss/perplexity/accuracy for the current epoch, up to the current step count. The last step of the epoch when it ends represents the total average loss/perplexity/accuracy for that epoch; it is recommended to use the accuracy metric.lr：the learning rate for the current step.[('loss_att', 0.259), ('acc', 0.825), ('loss_pre', 0.04), ('loss', 0.299), ('batch_size', 40)]：the specific data for the current GPU ID.total_time：the total time taken for a single step.GPU, memory：the model-used/peak memory and the model+cache-used/peak memory.
tensorboard --logdir /xxxx/FunASR/examples/industrial_data_pretraining/paraformer/outputs/log/tensorboardAssuming the training model path is: ./model_dir, if a configuration.json file has been generated in this directory, you only need to change the model name to the model path in the above model inference method.
For example, for shell inference:
python -m funasr.bin.inference ++model="./model_dir" ++input=="${input}" ++output_dir="${output_dir}"Python inference
from funasr import AutoModel
model = AutoModel(model="./model_dir")
res = model.generate(input=wav_file)
print(res)If there is no configuration.json in the model path, you need to manually specify the exact configuration file path and the model path.
python -m funasr.bin.inference \
--config-path "${local_path}" \
--config-name "${config}" \
++init_param="${init_param}" \
++tokenizer_conf.token_list="${tokens}" \
++frontend_conf.cmvn_file="${cmvn_file}" \
++input="${input}" \
++output_dir="${output_dir}" \
++device="${device}"Parameter Introduction
config-path：This is the path to the config.yaml saved during the experiment, which can be found in the experiment's output directory.config-name：The name of the configuration file, usually config.yaml. It supports both YAML and JSON formats, for example config.json.init_param：The model parameters that need to be tested, usually model.pt. You can choose a specific model file as needed.tokenizer_conf.token_list：The path to the vocabulary file, which is normally specified in config.yaml. There is no need to manually specify it again unless the path in config.yaml is incorrect, in which case the correct path must be manually specified here.frontend_conf.cmvn_file：The CMVN (Cepstral Mean and Variance Normalization) file used when extracting fbank features from WAV files, which is usually specified in config.yaml. There is no need to manually specify it again unless the path in config.yaml is incorrect, in which case the correct path must be manually specified here.
Other parameters are the same as mentioned above. A complete example can be found here.
funasr-export ++model=paraformer ++quantize=false ++device=cpufrom funasr import AutoModel
model = AutoModel(model="paraformer", device="cpu")
res = model.export(quantize=False)# pip3 install -U funasr-onnx
from funasr_onnx import Paraformer
model_dir = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model = Paraformer(model_dir, batch_size=1, quantize=True)
wav_path = ['~/.cache/modelscope/hub/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav']
result = model(wav_path)
print(result)More examples ref to demo