funasr1.0.5 (#1328)

examples/industrial_data_pretraining/conformer/demo.py

@@ -0,0 +1,13 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
+from funasr import AutoModel
+
+model = AutoModel(model="iic/speech_conformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch", model_revision="v2.0.4",
+ )
+
+res = model.generate(input="https://modelscope.oss-cn-beijing.aliyuncs.com/test/audios/asr_example.wav")
+print(res)
+

examples/industrial_data_pretraining/conformer/infer.sh

@@ -0,0 +1,11 @@
+
+model="iic/speech_conformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch"
+model_revision="v2.0.4"
+
+python funasr/bin/inference.py \
++model=${model} \
++model_revision=${model_revision} \
++input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" \
++output_dir="./outputs/debug" \
++device="cpu" \
+

funasr/models/conformer/template.yaml

@@ -6,8 +6,7 @@
 # tables.print()
 
 # network architecture
-#model: funasr.models.paraformer.model:Paraformer
-model: Transformer
+model: Conformer
 model_conf:
  ctc_weight: 0.3
  lsm_weight: 0.1 # label smoothing option
@@ -16,14 +15,14 @@ model_conf:
 # encoder
 encoder: ConformerEncoder
 encoder_conf:
- output_size: 256 # dimension of attention
+ output_size: 256
  attention_heads: 4
- linear_units: 2048 # the number of units of position-wise feed forward
- num_blocks: 12 # the number of encoder blocks
+ linear_units: 2048
+ num_blocks: 12
  dropout_rate: 0.1
  positional_dropout_rate: 0.1
  attention_dropout_rate: 0.0
- input_layer: conv2d # encoder architecture type
+ input_layer: conv2d
  normalize_before: true
  pos_enc_layer_type: rel_pos
  selfattention_layer_type: rel_selfattn
@@ -52,6 +51,7 @@ frontend_conf:
  n_mels: 80
  frame_length: 25
  frame_shift: 10
+ dither: 0.0
  lfr_m: 1
  lfr_n: 1
 

funasr/models/transformer/model.py

@@ -24,18 +24,16 @@ class Transformer(nn.Module):
 
  def __init__(
  self,
- frontend: Optional[str] = None,
- frontend_conf: Optional[Dict] = None,
- specaug: Optional[str] = None,
- specaug_conf: Optional[Dict] = None,
+ specaug: str = None,
+ specaug_conf: dict = None,
  normalize: str = None,
- normalize_conf: Optional[Dict] = None,
+ normalize_conf: dict = None,
  encoder: str = None,
- encoder_conf: Optional[Dict] = None,
+ encoder_conf: dict = None,
  decoder: str = None,
- decoder_conf: Optional[Dict] = None,
+ decoder_conf: dict = None,
  ctc: str = None,
- ctc_conf: Optional[Dict] = None,
+ ctc_conf: dict = None,
  ctc_weight: float = 0.5,
  interctc_weight: float = 0.0,
  input_size: int = 80,
@@ -59,20 +57,17 @@ def __init__(
 
  super().__init__()
 
- if frontend is not None:
- frontend_class = tables.frontend_classes.get_class(frontend)
- frontend = frontend_class(**frontend_conf)
  if specaug is not None:
- specaug_class = tables.specaug_classes.get_class(specaug)
+ specaug_class = tables.specaug_classes.get(specaug)
  specaug = specaug_class(**specaug_conf)
  if normalize is not None:
- normalize_class = tables.normalize_classes.get_class(normalize)
+ normalize_class = tables.normalize_classes.get(normalize)
  normalize = normalize_class(**normalize_conf)
- encoder_class = tables.encoder_classes.get_class(encoder)
+ encoder_class = tables.encoder_classes.get(encoder)
  encoder = encoder_class(input_size=input_size, **encoder_conf)
  encoder_output_size = encoder.output_size()
  if decoder is not None:
- decoder_class = tables.decoder_classes.get_class(decoder)
+ decoder_class = tables.decoder_classes.get(decoder)
  decoder = decoder_class(
  vocab_size=vocab_size,
  encoder_output_size=encoder_output_size,
@@ -93,7 +88,6 @@ def __init__(
  self.vocab_size = vocab_size
  self.ignore_id = ignore_id
  self.ctc_weight = ctc_weight
- self.frontend = frontend
  self.specaug = specaug
  self.normalize = normalize
  self.encoder = encoder
@@ -338,6 +332,7 @@ def init_beam_search(self,
  )
  token_list = kwargs.get("token_list")
  scorers.update(
+ decoder=self.decoder,
  length_bonus=LengthBonus(len(token_list)),
  )
 
@@ -348,14 +343,14 @@ def init_beam_search(self,
  scorers["ngram"] = ngram
 
  weights = dict(
- decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.0),
- ctc=kwargs.get("decoding_ctc_weight", 0.0),
+ decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.5),
+ ctc=kwargs.get("decoding_ctc_weight", 0.5),
  lm=kwargs.get("lm_weight", 0.0),
  ngram=kwargs.get("ngram_weight", 0.0),
  length_bonus=kwargs.get("penalty", 0.0),
  )
  beam_search = BeamSearch(
- beam_size=kwargs.get("beam_size", 2),
+ beam_size=kwargs.get("beam_size", 10),
  weights=weights,
  scorers=scorers,
  sos=self.sos,
@@ -364,45 +359,50 @@ def init_beam_search(self,
  token_list=token_list,
  pre_beam_score_key=None if self.ctc_weight == 1.0 else "full",
  )
- # beam_search.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
- # for scorer in scorers.values():
- # if isinstance(scorer, torch.nn.Module):
- # scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
+
  self.beam_search = beam_search
 
- def generate(self,
- data_in: list,
- data_lengths: list=None,
+ def inference(self,
+ data_in,
+ data_lengths=None,
  key: list=None,
  tokenizer=None,
+ frontend=None,
  **kwargs,
  ):
 
  if kwargs.get("batch_size", 1) > 1:
  raise NotImplementedError("batch decoding is not implemented")
 
  # init beamsearch
- is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
- is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
- if self.beam_search is None and (is_use_lm or is_use_ctc):
+ if self.beam_search is None:
  logging.info("enable beam_search")
  self.init_beam_search(**kwargs)
  self.nbest = kwargs.get("nbest", 1)
- 
+
  meta_data = {}
- # extract fbank feats
- time1 = time.perf_counter()
- audio_sample_list = load_audio_text_image_video(data_in, fs=self.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)
- time3 = time.perf_counter()
- meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
- meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
-
+ if isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank": # 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_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000),
+ data_type=kwargs.get("data_type", "sound"),
+ tokenizer=tokenizer)
+ 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=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
+
  speech = speech.to(device=kwargs["device"])
  speech_lengths = speech_lengths.to(device=kwargs["device"])
-
  # Encoder
  encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
  if isinstance(encoder_out, tuple):
@@ -439,14 +439,13 @@ def generate(self,
  token = tokenizer.ids2tokens(token_int)
  text = tokenizer.tokens2text(token)
 
- text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
- result_i = {"key": key[i], "token": token, "text": text, "text_postprocessed": text_postprocessed}
+ # text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
+ result_i = {"key": key[i], "token": token, "text": text}
  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["text_postprocessed"][key[i]] = text_postprocessed
 
  return results, meta_data
 

funasr/models/transformer/search.py

@@ -9,7 +9,7 @@
 
 import torch
 
-from funasr.metrics import end_detect
+from funasr.metrics.common import end_detect
 from funasr.models.transformer.scorers.scorer_interface import PartialScorerInterface
 from funasr.models.transformer.scorers.scorer_interface import ScorerInterface
 

funasr/models/transformer/template.yaml

@@ -6,7 +6,6 @@
 # tables.print()
 
 # network architecture
-#model: funasr.models.paraformer.model:Paraformer
 model: Transformer
 model_conf:
  ctc_weight: 0.3