From e9d2cfc3a134b00f4e98271fbee3838d1ccecbcc Mon Sep 17 00:00:00 2001
From: VirtuosoQ <2416050435@qq.com>
Date: 星期五, 26 四月 2024 14:59:30 +0800
Subject: [PATCH] FunASR java http client
---
funasr/models/sense_voice/model.py | 213 ++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 211 insertions(+), 2 deletions(-)
diff --git a/funasr/models/sense_voice/model.py b/funasr/models/sense_voice/model.py
index b5272a1..41ad880 100644
--- a/funasr/models/sense_voice/model.py
+++ b/funasr/models/sense_voice/model.py
@@ -209,7 +209,7 @@
language = None if language == "auto" else language
DecodingOptions["language"] = language
- DecodingOptions["vocab_path"] = kwargs.get("vocab_path", None)
+ DecodingOptions["vocab_path"] = kwargs["tokenizer_conf"].get("vocab_path", None)
if "without_timestamps" not in DecodingOptions:
@@ -226,4 +226,213 @@
results.append(result_i)
return results, meta_data
-
\ No newline at end of file
+
+
+@tables.register("model_classes", "SenseVoiceRWKV")
+class SenseVoiceRWKV(nn.Module):
+ def __init__(self, *args, **kwargs):
+ super().__init__()
+
+ dims = kwargs.get("dims", {})
+ dims = whisper.model.ModelDimensions(**dims)
+ model = whisper.model.Whisper(dims=dims)
+
+ # encoder
+ model.encoder.downsample_rate = kwargs.get("downsample_rate", 4)
+ model.encoder.use_padmask = kwargs.get("use_padmask", True)
+ from .encoder import sense_voice_encode_forward
+ model.encoder.forward = types.MethodType(sense_voice_encode_forward, model.encoder)
+
+ # decoder
+ del model.decoder
+ decoder = kwargs.get("decoder", "SenseVoiceDecoder")
+ decoder_class = tables.decoder_classes.get(decoder)
+ decoder = decoder_class(n_vocab=dims.n_vocab,
+ n_ctx=dims.n_text_ctx,
+ n_state=dims.n_text_state,
+ n_head=dims.n_text_head,
+ n_layer=dims.n_text_layer,
+ **kwargs.get("decoder_conf"))
+ model.decoder = decoder
+
+ self.model = model
+
+ self.encoder_output_size = self.model.dims.n_audio_state
+
+ self.activation_checkpoint = kwargs.get("activation_checkpoint", False)
+ self.ignore_id = kwargs.get("ignore_id", -1)
+ self.vocab_size = kwargs.get("vocab_size", -1)
+ self.length_normalized_loss = kwargs.get("length_normalized_loss", True)
+ self.criterion_att = LabelSmoothingLoss(
+ size=self.vocab_size,
+ padding_idx=self.ignore_id,
+ smoothing=kwargs.get("lsm_weight", 0.0),
+ normalize_length=self.length_normalized_loss,
+ )
+
+ specaug = kwargs.get("specaug", None)
+ if specaug is not None:
+ specaug_class = tables.specaug_classes.get(specaug)
+ specaug = specaug_class(**kwargs.get("specaug_conf", {}))
+ self.specaug = specaug
+
+ def forward(
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ text: torch.Tensor,
+ text_lengths: torch.Tensor,
+ **kwargs,
+ ):
+ target_mask = kwargs.get("target_mask", None)
+
+ # import pdb;
+ # pdb.set_trace()
+ if len(text_lengths.size()) > 1:
+ text_lengths = text_lengths[:, 0]
+ if len(speech_lengths.size()) > 1:
+ speech_lengths = speech_lengths[:, 0]
+
+ batch_size = speech.shape[0]
+
+ if self.activation_checkpoint:
+ from torch.utils.checkpoint import checkpoint
+ encoder_out, encoder_out_lens = checkpoint(self.encode, speech, speech_lengths, use_reentrant=False)
+ else:
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+
+ loss_att, acc_att, cer_att, wer_att = self._calc_att_loss(
+ encoder_out, encoder_out_lens, text, text_lengths, target_mask=target_mask
+ )
+ loss = loss_att
+ stats = {}
+ stats["acc"] = acc_att
+ stats["loss"] = torch.clone(loss.detach())
+ stats["batch_size"] = batch_size
+
+ # force_gatherable: to-device and to-tensor if scalar for DataParallel
+ if self.length_normalized_loss:
+ batch_size = int((text_lengths + 1).sum())
+ loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
+ return loss, stats, weight
+
+ def encode(
+ self, speech: torch.Tensor, speech_lengths: torch.Tensor, **kwargs,
+ ):
+ """Encoder. Note that this method is used by asr_inference.py
+ Args:
+ speech: (Batch, Length, ...)
+ speech_lengths: (Batch, )
+ ind: int
+ """
+ with autocast(False):
+ # Data augmentation
+ if self.specaug is not None and self.training:
+ speech, speech_lengths = self.specaug(speech, speech_lengths)
+
+ # Forward encoder
+ encoder_out, encoder_out_lens = self.model.encoder(speech.permute(0, 2, 1), speech_lengths)
+
+ return encoder_out, encoder_out_lens
+
+ def _calc_att_loss(
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
+ **kwargs,
+ ):
+ target_mask = kwargs.get("target_mask", None)
+ stats = {}
+
+ # 1. Forward decoder
+ decoder_out = self.model.decoder(
+ x=ys_pad, xa=encoder_out, hlens=encoder_out_lens, ys_in_lens=ys_pad_lens
+ )
+
+ # 2. Compute attention loss
+ mask = torch.ones_like(ys_pad) * (-1)
+ ys_pad_mask = (ys_pad * target_mask + mask * (1 - target_mask)).to(torch.int64)
+ ys_pad_mask[ys_pad_mask == 0] = -1
+ loss_att = self.criterion_att(decoder_out[:, :-1, :], ys_pad_mask[:, 1:])
+
+ with torch.no_grad():
+ preds = torch.argmax(decoder_out, -1)
+ acc_att = compute_accuracy(preds[:, :-1], ys_pad_mask[:, 1:], ignore_label=self.ignore_id)
+
+ return loss_att, acc_att, None, 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")
+
+ if frontend is None and not hasattr(self, "frontend"):
+ frontend_class = tables.frontend_classes.get("WhisperFrontend")
+ frontend = frontend_class(n_mels=self.model.dims.n_mels, do_pad_trim=kwargs.get("do_pad_trim", True))
+ self.frontend = frontend
+ else:
+ frontend = frontend if frontend is not None else self.frontend
+
+ meta_data = {}
+ 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 if hasattr(frontend, "fs") else 16000,
+ 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}"
+ frame_shift = frontend.frame_shift if hasattr(frontend, "frame_shift") else 10
+ lfr_n = frontend.lfr_n if hasattr(frontend, "lfr_n") else 1
+ meta_data["batch_data_time"] = speech_lengths.sum().item() * frame_shift * lfr_n / 1000
+
+ speech = speech.to(device=kwargs["device"])[0, :, :]
+ speech_lengths = speech_lengths.to(device=kwargs["device"])
+
+ DecodingOptions = kwargs.get("DecodingOptions", {})
+ task = DecodingOptions.get("task", "ASR")
+ if isinstance(task, str):
+ task = [task]
+ task = "".join([f"<|{x}|>" for x in task])
+ initial_prompt = kwargs.get("initial_prompt", f"<|startoftranscript|>{task}")
+ DecodingOptions["initial_prompt"] = initial_prompt
+
+ language = DecodingOptions.get("language", None)
+ language = None if language == "auto" else language
+ DecodingOptions["language"] = language
+
+ DecodingOptions["vocab_path"] = kwargs["tokenizer_conf"].get("vocab_path", None)
+
+ if "without_timestamps" not in DecodingOptions:
+ DecodingOptions["without_timestamps"] = True
+
+ options = whisper.DecodingOptions(**DecodingOptions)
+
+ result = whisper.decode(self.model, speech, options)
+ text = f"{result.text}"
+ results = []
+ result_i = {"key": key[0], "text": text}
+
+ results.append(result_i)
+
+ return results, meta_data
--
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