vad + asr

游雁

2023-12-21 5a8f37908469d9550f905ba0876c7c4e6f9b8026

vad + asr

 examples/industrial_data_pretraining/paraformer-large-long/infer.sh

New file
@@ -0,0 +1,31 @@

cmd="funasr/bin/inference.py"

python $cmd \
+model="/Users/zhifu/Downloads/modelscope_models/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
+vad_model="/Users/zhifu/Downloads/modelscope_models/speech_fsmn_vad_zh-cn-16k-common-pytorch" \
+input="/Users/zhifu/funasr_github/test_local/vad_example.wav" \
+output_dir="/Users/zhifu/Downloads/ckpt/funasr2/exp2" \
+device="cpu" \
+batch_size_s=300 \
+batch_size_threshold_s=60 \
+debug="true"

#python $cmd \
#+model="/Users/zhifu/Downloads/modelscope_models/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404" \
#+input="/Users/zhifu/Downloads/asr_example.wav" \
#+output_dir="/Users/zhifu/Downloads/ckpt/funasr2/exp2" \
#+device="cpu" \
#+"hotword='达魔院 魔搭'"

#+input="/Users/zhifu/funasr_github/test_local/wav.scp"
#+input="/Users/zhifu/funasr_github/test_local/asr_example.wav" \
#+input="/Users/zhifu/funasr_github/test_local/aishell2_dev_ios/asr_task_debug_len.jsonl" \
#+input="/Users/zhifu/funasr_github/test_local/aishell2_dev_ios/asr_task_debug_len_10.jsonl" \
#+model="/Users/zhifu/modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \

#+model="/Users/zhifu/modelscope_models/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
#+model="/Users/zhifu/modelscope_models/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404" \
#+"hotword='达魔院 魔搭'"

#+vad_model="/Users/zhifu/Downloads/modelscope_models/speech_fsmn_vad_zh-cn-16k-common-pytorch" \

 funasr/bin/inference.py

@@ -16,7 +16,8 @@
import random
import string
from funasr.register import tables

from funasr.datasets.audio_datasets.load_audio_extract_fbank import load_audio
from funasr.utils.vad_utils import slice_padding_audio_samples

def build_iter_for_infer(data_in, input_len=None, data_type="sound"):
    """
@@ -73,15 +74,44 @@

    logging.basicConfig(level=log_level)

    import pdb;
    pdb.set_trace()
    if kwargs.get("debug", False):
        import pdb; pdb.set_trace()
    model = AutoModel(**kwargs)
    res = model.generate(input=kwargs["input"])
    res = model(input=kwargs["input"])
    print(res)

class AutoModel:
	
    def __init__(self, **kwargs):
        tables.print()
		
        model, kwargs = self.build_model(**kwargs)
		
        # if vad_model is not None, build vad model else None
        vad_model = kwargs.get("vad_model", None)
        vad_kwargs = kwargs.get("vad_model_revision", None)
        if vad_model is not None:
            print("build vad model")
            vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs}
            vad_model, vad_kwargs = self.build_model(**vad_kwargs)

        # if punc_model is not None, build punc model else None
        punc_model = kwargs.get("punc_model", None)
        punc_kwargs = kwargs.get("punc_model_revision", None)
        if punc_model is not None:
            punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs}
            punc_model, punc_kwargs = self.build_model(**punc_kwargs)
			
        self.kwargs = kwargs
        self.model = model
        self.vad_model = vad_model
        self.vad_kwargs = vad_kwargs
        self.punc_model = punc_model
        self.punc_kwargs = punc_kwargs
		
		

    def build_model(self, **kwargs):
        assert "model" in kwargs
        if "model_conf" not in kwargs:
            logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
@@ -113,7 +143,8 @@
        
        # build model
        model_class = tables.model_classes.get(kwargs["model"].lower())
        model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=len(tokenizer.token_list) if tokenizer is not None else -1)
        model = model_class(**kwargs, **kwargs["model_conf"],
                            vocab_size=len(tokenizer.token_list) if tokenizer is not None else -1)
        model.eval()
        model.to(device)
        
@@ -127,23 +158,34 @@
                ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
                oss_bucket=kwargs.get("oss_bucket", None),
            )
        self.kwargs = kwargs
        self.model = model
        self.tokenizer = tokenizer
    
    def generate(self, input, input_len=None, **cfg):
        self.kwargs.update(cfg)
        data_type = self.kwargs.get("data_type", "sound")
        batch_size = self.kwargs.get("batch_size", 1)
        if self.kwargs.get("device", "cpu") == "cpu":
            batch_size = 1
        return model, kwargs
	
    def __call__(self, input, input_len=None, **cfg):
        if self.vad_model is None:
            return self.generate(input, input_len=input_len, **cfg)
			
        else:
            return self.generate_with_vad(input, input_len=input_len, **cfg)
		
    def generate(self, input, input_len=None, model=None, kwargs=None, **cfg):
        kwargs = self.kwargs if kwargs is None else kwargs
        kwargs.update(cfg)
        model = self.model if model is None else model
		
        data_type = kwargs.get("data_type", "sound")
        batch_size = kwargs.get("batch_size", 1)
        # if kwargs.get("device", "cpu") == "cpu":
        #     batch_size = 1
        
        key_list, data_list = build_iter_for_infer(input, input_len=input_len, data_type=data_type)
        
        speed_stats = {}
        asr_result_list = []
        num_samples = len(data_list)
        pbar = tqdm(colour="blue", total=num_samples, dynamic_ncols=True)
        pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True)
        time_speech_total = 0.0
        time_escape_total = 0.0
        for beg_idx in range(0, num_samples, batch_size):
            end_idx = min(num_samples, beg_idx + batch_size)
            data_batch = data_list[beg_idx:end_idx]
@@ -154,25 +196,139 @@
                batch["data_lengths"] = input_len
        
            time1 = time.perf_counter()
            results, meta_data = self.model.generate(**batch, **self.kwargs)
            results, meta_data = model.generate(**batch, **kwargs)
            time2 = time.perf_counter()
            
            asr_result_list.append(results)
            asr_result_list.extend(results)
            pbar.update(1)
            
            # batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
            batch_data_time = meta_data.get("batch_data_time", -1)
            time_escape = time2 - time1
            speed_stats["load_data"] = meta_data.get("load_data", 0.0)
            speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
            speed_stats["forward"] = f"{time2 - time1:0.3f}"
            speed_stats["rtf"] = f"{(time2 - time1) / batch_data_time:0.3f}"
            speed_stats["forward"] = f"{time_escape:0.3f}"
            speed_stats["batch_size"] = f"{len(results)}"
            speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
            description = (
                f"{speed_stats}, "
            )
            pbar.set_description(description)
            time_speech_total += batch_data_time
            time_escape_total += time_escape
        
        pbar.update(1)
        pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
        torch.cuda.empty_cache()
        return asr_result_list

    def generate_with_vad(self, input, input_len=None, **cfg):
		
        # step.1: compute the vad model
        model = self.vad_model
        kwargs = self.vad_kwargs
        beg_vad = time.time()
        res = self.generate(input, input_len=input_len, model=model, kwargs=kwargs, **cfg)
        end_vad = time.time()
        print(f"time cost vad: {end_vad - beg_vad:0.3f}")


        # step.2 compute asr model
        model = self.model
        kwargs = self.kwargs
        kwargs.update(cfg)
        batch_size = int(kwargs.get("batch_size_s", 300))*1000
        batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
        kwargs["batch_size"] = batch_size
        data_type = kwargs.get("data_type", "sound")
        key_list, data_list = build_iter_for_infer(input, input_len=input_len, data_type=data_type)
        results_ret_list = []
        time_speech_total_all_samples = 0.0

        beg_total = time.time()
        pbar_total = tqdm(colour="red", total=len(res) + 1, dynamic_ncols=True)
        for i in range(len(res)):
            key = res[i]["key"]
            vadsegments = res[i]["value"]
            input_i = data_list[i]
            speech = load_audio(input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000))
            speech_lengths = len(speech)
            n = len(vadsegments)
            data_with_index = [(vadsegments[i], i) for i in range(n)]
            sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
            results_sorted = []
			
            if not len(sorted_data):
                logging.info("decoding, utt: {}, empty speech".format(key))
                continue
			

            # if kwargs["device"] == "cpu":
            #     batch_size = 0
            if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
                batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0])
			
            batch_size_ms_cum = 0
            beg_idx = 0
            beg_asr_total = time.time()
            time_speech_total_per_sample = speech_lengths/16000
            time_speech_total_all_samples += time_speech_total_per_sample

            for j, _ in enumerate(range(0, n)):
                batch_size_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
                if j < n - 1 and (
                    batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size and (
                    sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_threshold_ms:
                    continue
                batch_size_ms_cum = 0
                end_idx = j + 1
                speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])
                beg_idx = end_idx

                results = self.generate(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
	
                if len(results) < 1:
                    continue
                results_sorted.extend(results)


            pbar_total.update(1)
            end_asr_total = time.time()
            time_escape_total_per_sample = end_asr_total - beg_asr_total
            pbar_total.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
                                 f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
                                 f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")

            restored_data = [0] * n
            for j in range(n):
                index = sorted_data[j][1]
                restored_data[index] = results_sorted[j]
            result = {}
			
            for j in range(n):
                for k, v in restored_data[j].items():
                    if not k.startswith("timestamp"):
                        if k not in result:
                            result[k] = restored_data[j][k]
                        else:
                            result[k] += restored_data[j][k]
                    else:
                        result[k] = []
                        for t in restored_data[j][k]:
                            t[0] += vadsegments[j][0]
                            t[1] += vadsegments[j][0]
                        result[k] += restored_data[j][k]
						
            result["key"] = key
            results_ret_list.append(result)
            pbar_total.update(1)
        pbar_total.update(1)
        end_total = time.time()
        time_escape_total_all_samples = end_total - beg_total
        pbar_total.set_description(f"rtf_avg_all_samples: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
                             f"time_speech_total_all_samples: {time_speech_total_all_samples: 0.3f}, "
                             f"time_escape_total_all_samples: {time_escape_total_all_samples:0.3f}")
        return results_ret_list
	
if __name__ == '__main__':
    main_hydra()

 funasr/bin/train.py

@@ -25,7 +25,9 @@

@hydra.main(config_name=None, version_base=None)
def main_hydra(kwargs: DictConfig):
    if kwargs.get("debug", False):
    import pdb; pdb.set_trace()

    assert "model" in kwargs
    if "model_conf" not in kwargs:
        logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))

 funasr/download/download_from_hub.py

@@ -24,11 +24,10 @@
    kwargs["init_param"] = init_param
    if os.path.exists(os.path.join(model_or_path, "tokens.txt")):
        kwargs["tokenizer_conf"]["token_list"] = os.path.join(model_or_path, "tokens.txt")
    if os.path.exists(os.path.join(model_or_path, "tokens.txt")):
    if os.path.exists(os.path.join(model_or_path, "seg_dict")):
        kwargs["tokenizer_conf"]["seg_dict"] = os.path.join(model_or_path, "seg_dict")
    if os.path.exists(os.path.join(model_or_path, "bpe.model")):
        kwargs["tokenizer_conf"]["bpemodel"] = os.path.join(model_or_path, "bpe.model")
	
    kwargs["model"] = cfg["model"]
    kwargs["frontend_conf"]["cmvn_file"] = os.path.join(model_or_path, "am.mvn")
    

 funasr/models/bici_paraformer/model.py

@@ -29,6 +29,7 @@
from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
from funasr.register import tables
from funasr.models.ctc.ctc import CTC
from funasr.utils.timestamp_tools import time_stamp_sentence

from funasr.models.paraformer.model import Paraformer

@@ -212,9 +213,10 @@
        loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
        return loss, stats, weight
    

    def generate(self,
                 data_in: list,
                 data_lengths: list = None,
                 data_in,
                 data_lengths=None,
                 key: list = None,
                 tokenizer=None,
                 frontend=None,
@@ -230,17 +232,23 @@
            self.nbest = kwargs.get("nbest", 1)
        
        meta_data = {}
        if isinstance(data_in, torch.Tensor):  # fbank
            speech, speech_lengths = data_in, data_lengths
            if len(speech.shape) < 3:
                speech = speech[None, :, :]
            if speech_lengths is None:
                speech_lengths = speech.shape[1]
        else:
        # extract fbank feats
        time1 = time.perf_counter()
        audio_sample_list = load_audio(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
        time2 = time.perf_counter()
        meta_data["load_data"] = f"{time2 - time1:0.3f}"
        speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
                                               frontend=self.frontend)
                                                   frontend=frontend)
        time3 = time.perf_counter()
        meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
        meta_data[
            "batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
            meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
        
        speech.to(device=kwargs["device"]), speech_lengths.to(device=kwargs["device"])
        
@@ -261,7 +269,6 @@
        decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
        
        # BiCifParaformer, test no bias cif2

        _, _, us_alphas, us_peaks = self.calc_predictor_timestamp(encoder_out, encoder_out_lens,
                                                                                pre_token_length)
        
@@ -302,6 +309,7 @@
                # remove blank symbol id, which is assumed to be 0
                token_int = list(filter(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))
                
                if tokenizer is not None:
                # Change integer-ids to tokens
                token = tokenizer.ids2tokens(token_int)
                text = tokenizer.tokens2text(token)
@@ -311,18 +319,22 @@
                                                           copy.copy(token),
                                                           vad_offset=kwargs.get("begin_time", 0))
                
                text_postprocessed, time_stamp_postprocessed, word_lists = postprocess_utils.sentence_postprocess(token, timestamp)
				
                    text_postprocessed, time_stamp_postprocessed, word_lists = postprocess_utils.sentence_postprocess(
                        token, timestamp)
                    sentences = time_stamp_sentence(None, time_stamp_postprocessed, text_postprocessed)
                result_i = {"key": key[i], "token": token, "text": text, "text_postprocessed": text_postprocessed,
                            "time_stamp_postprocessed": time_stamp_postprocessed,
                            "word_lists": word_lists
                                "timestamp": time_stamp_postprocessed,
                                "word_lists": word_lists,
                                "sentences": sentences
                            }
                results.append(result_i)
                
                if ibest_writer is not None:
                    ibest_writer["token"][key[i]] = " ".join(token)
                    ibest_writer["text"][key[i]] = text
                        ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed
                    ibest_writer["text_postprocessed"][key[i]] = text_postprocessed
					
                else:
                    result_i = {"key": key[i], "token_int": token_int}
                results.append(result_i)
        
        return results, meta_data

 funasr/models/bici_paraformer/template.yaml

New file
@@ -0,0 +1,134 @@
# This is an example that demonstrates how to configure a model file.
# You can modify the configuration according to your own requirements.

# to print the register_table:
# from funasr.register import tables
# tables.print()

# network architecture
#model: funasr.models.paraformer.model:Paraformer
model: BiCifParaformer
model_conf:
    ctc_weight: 0.0
    lsm_weight: 0.1
    length_normalized_loss: true
    predictor_weight: 1.0
    predictor_bias: 1
    sampling_ratio: 0.75

# encoder
encoder: SANMEncoder
encoder_conf:
    output_size: 512
    attention_heads: 4
    linear_units: 2048
    num_blocks: 50
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.1
    input_layer: pe
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shfit: 0
    selfattention_layer_type: sanm

# decoder
decoder: ParaformerSANMDecoder
decoder_conf:
    attention_heads: 4
    linear_units: 2048
    num_blocks: 16
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    self_attention_dropout_rate: 0.1
    src_attention_dropout_rate: 0.1
    att_layer_num: 16
    kernel_size: 11
    sanm_shfit: 0

predictor: CifPredictorV3
predictor_conf:
    idim: 512
    threshold: 1.0
    l_order: 1
    r_order: 1
    tail_threshold: 0.45
    smooth_factor2: 0.25
    noise_threshold2: 0.01
    upsample_times: 3
    use_cif1_cnn: false
    upsample_type: cnn_blstm

# frontend related
frontend: WavFrontend
frontend_conf:
    fs: 16000
    window: hamming
    n_mels: 80
    frame_length: 25
    frame_shift: 10
    lfr_m: 7
    lfr_n: 6

specaug: SpecAugLFR
specaug_conf:
    apply_time_warp: false
    time_warp_window: 5
    time_warp_mode: bicubic
    apply_freq_mask: true
    freq_mask_width_range:
    - 0
    - 30
    lfr_rate: 6
    num_freq_mask: 1
    apply_time_mask: true
    time_mask_width_range:
    - 0
    - 12
    num_time_mask: 1

train_conf:
  accum_grad: 1
  grad_clip: 5
  max_epoch: 150
  val_scheduler_criterion:
      - valid
      - acc
  best_model_criterion:
  -   - valid
      - acc
      - max
  keep_nbest_models: 10
  log_interval: 50

optim: adam
optim_conf:
   lr: 0.0005
scheduler: warmuplr
scheduler_conf:
   warmup_steps: 30000

dataset: AudioDataset
dataset_conf:
    index_ds: IndexDSJsonl
    batch_sampler: DynamicBatchLocalShuffleSampler
    batch_type: example # example or length
    batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len;
    max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length,
    buffer_size: 500
    shuffle: True
    num_workers: 0

tokenizer: CharTokenizer
tokenizer_conf:
  unk_symbol: <unk>
  split_with_space: true


ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: true
normalize: null

 funasr/models/paraformer_streaming/__init__.py

 funasr/models/paraformer_streaming/model.py

File was renamed from funasr/models/paraformer_online/model.py
@@ -857,7 +857,7 @@
        return results, meta_data


class ParaformerOnline(Paraformer):
class ParaformerStreaming(Paraformer):
    """
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition

 funasr/models/paraformer_streaming/sanm_decoder.py

 funasr/utils/vad_utils.py

@@ -16,3 +16,16 @@
    speech_lengths_pad = torch.Tensor(speech_lengths_list).int()
    return feats_pad, speech_lengths_pad
    

def slice_padding_audio_samples(speech, speech_lengths, vad_segments):
    speech_list = []
    speech_lengths_list = []
    for i, segment in enumerate(vad_segments):
        bed_idx = int(segment[0][0] * 16)
        end_idx = min(int(segment[0][1] * 16), speech_lengths)
        speech_i = speech[bed_idx: end_idx]
        speech_lengths_i = end_idx - bed_idx
        speech_list.append(speech_i)
        speech_lengths_list.append(speech_lengths_i)
		
    return speech_list, speech_lengths_list