From 4ace5a95b052d338947fc88809a440ccd55cf6b4 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 16 十一月 2023 16:39:52 +0800
Subject: [PATCH] funasr pages
---
funasr/bin/asr_infer.py | 140 ++++++++++++++++++++++++++++++++++++++++++++--
1 files changed, 132 insertions(+), 8 deletions(-)
diff --git a/funasr/bin/asr_infer.py b/funasr/bin/asr_infer.py
index 2e002b7..7015eb8 100644
--- a/funasr/bin/asr_infer.py
+++ b/funasr/bin/asr_infer.py
@@ -399,7 +399,7 @@
@torch.no_grad()
def __call__(
self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
- begin_time: int = 0, end_time: int = None,
+ decoding_ind: int = None, begin_time: int = 0, end_time: int = None,
):
"""Inference
@@ -429,7 +429,9 @@
batch = to_device(batch, device=self.device)
# b. Forward Encoder
- enc, enc_len = self.asr_model.encode(**batch, ind=self.decoding_ind)
+ if decoding_ind is None:
+ decoding_ind = 0 if self.decoding_ind is None else self.decoding_ind
+ enc, enc_len = self.asr_model.encode(**batch, ind=decoding_ind)
if isinstance(enc, tuple):
enc = enc[0]
# assert len(enc) == 1, len(enc)
@@ -1335,7 +1337,7 @@
quantize_dtype: str = "qint8",
nbest: int = 1,
streaming: bool = False,
- simu_streaming: bool = False,
+ fake_streaming: bool = False,
full_utt: bool = False,
chunk_size: int = 16,
left_context: int = 32,
@@ -1430,7 +1432,7 @@
self.beam_search = beam_search
self.streaming = streaming
- self.simu_streaming = simu_streaming
+ self.fake_streaming = fake_streaming
self.full_utt = full_utt
self.chunk_size = max(chunk_size, 0)
self.left_context = left_context
@@ -1440,8 +1442,8 @@
self.streaming = False
self.asr_model.encoder.dynamic_chunk_training = False
- if not simu_streaming or chunk_size == 0:
- self.simu_streaming = False
+ if not fake_streaming or chunk_size == 0:
+ self.fake_streaming = False
self.asr_model.encoder.dynamic_chunk_training = False
self.frontend = frontend
@@ -1518,7 +1520,7 @@
return nbest_hyps
@torch.no_grad()
- def simu_streaming_decode(self, speech: Union[torch.Tensor, np.ndarray]) -> List[HypothesisTransducer]:
+ def fake_streaming_decode(self, speech: Union[torch.Tensor, np.ndarray]) -> List[HypothesisTransducer]:
"""Speech2Text call.
Args:
speech: Speech data. (S)
@@ -1603,7 +1605,6 @@
feats_lengths = to_device(feats_lengths, device=self.device)
enc_out, _, _ = self.asr_model.encoder(feats, feats_lengths)
-
nbest_hyps = self.beam_search(enc_out[0])
return nbest_hyps
@@ -1878,3 +1879,126 @@
results.append((text, text_id, token, token_int, hyp))
return results
+
+
+class Speech2TextWhisper:
+ """Speech2Text class
+
+ Examples:
+ >>> import soundfile
+ >>> speech2text = Speech2Text("asr_config.yml", "asr.pb")
+ >>> audio, rate = soundfile.read("speech.wav")
+ >>> speech2text(audio)
+ [(text, token, token_int, hypothesis object), ...]
+
+ """
+
+ def __init__(
+ self,
+ asr_train_config: Union[Path, str] = None,
+ asr_model_file: Union[Path, str] = None,
+ cmvn_file: Union[Path, str] = None,
+ lm_train_config: Union[Path, str] = None,
+ lm_file: Union[Path, str] = None,
+ token_type: str = None,
+ bpemodel: str = None,
+ device: str = "cpu",
+ maxlenratio: float = 0.0,
+ minlenratio: float = 0.0,
+ batch_size: int = 1,
+ dtype: str = "float32",
+ beam_size: int = 20,
+ ctc_weight: float = 0.5,
+ lm_weight: float = 1.0,
+ ngram_weight: float = 0.9,
+ penalty: float = 0.0,
+ nbest: int = 1,
+ streaming: bool = False,
+ frontend_conf: dict = None,
+ language: str = None,
+ task: str = "transcribe",
+ **kwargs,
+ ):
+
+ from funasr.tasks.whisper import ASRTask
+
+ # 1. Build ASR model
+ scorers = {}
+ asr_model, asr_train_args = ASRTask.build_model_from_file(
+ asr_train_config, asr_model_file, cmvn_file, device
+ )
+ frontend = None
+
+ logging.info("asr_model: {}".format(asr_model))
+ logging.info("asr_train_args: {}".format(asr_train_args))
+ asr_model.to(dtype=getattr(torch, dtype)).eval()
+
+ decoder = asr_model.decoder
+
+ token_list = []
+
+ # 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
+ if token_type is None:
+ token_type = asr_train_args.token_type
+ if bpemodel is None:
+ bpemodel = asr_train_args.bpemodel
+
+ if token_type is None:
+ tokenizer = None
+ elif token_type == "bpe":
+ if bpemodel is not None:
+ tokenizer = build_tokenizer(token_type=token_type, bpemodel=bpemodel)
+ else:
+ tokenizer = None
+ else:
+ tokenizer = build_tokenizer(token_type=token_type)
+ logging.info(f"Text tokenizer: {tokenizer}")
+
+ self.asr_model = asr_model
+ self.asr_train_args = asr_train_args
+ self.tokenizer = tokenizer
+ self.device = device
+ self.dtype = dtype
+ self.frontend = frontend
+ self.language = language
+ self.task = task
+
+ @torch.no_grad()
+ def __call__(
+ self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None
+ ) -> List[
+ Tuple[
+ Optional[str],
+ List[str],
+ List[int],
+ Union[Hypothesis],
+ ]
+ ]:
+ """Inference
+
+ Args:
+ speech: Input speech data
+ Returns:
+ text, token, token_int, hyp
+
+ """
+
+ from funasr.utils.whisper_utils.transcribe import transcribe
+ from funasr.utils.whisper_utils.audio import pad_or_trim, log_mel_spectrogram
+ from funasr.utils.whisper_utils.decoding import DecodingOptions, detect_language, decode
+
+ speech = speech[0]
+ speech = pad_or_trim(speech)
+ mel = log_mel_spectrogram(speech).to(self.device)
+
+ if self.asr_model.is_multilingual:
+ options = DecodingOptions(fp16=False, language=self.language, task=self.task)
+ asr_res = decode(self.asr_model, mel, options)
+ text = asr_res.text
+ language = self.language if self.language else asr_res.language
+ else:
+ asr_res = transcribe(self.asr_model, speech, fp16=False)
+ text = asr_res["text"]
+ language = asr_res["language"]
+ results = [(text, language)]
+ return results
--
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