from dataclasses import dataclass
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from typing import Dict
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from typing import Iterable, Optional
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import time
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import numpy as np
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import torch
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import torch.nn.functional as F
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from torch import Tensor
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from torch import nn
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from . import whisper_lib as whisper
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from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
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from funasr.register import tables
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@tables.register("model_classes", "SenseVoice")
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class SenseVoice(nn.Module):
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def __init__(self, *args, **kwargs):
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super().__init__()
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hub = kwargs.get("hub", "funasr")
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dims = kwargs.get("dims", {})
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dims = whisper.model.ModelDimensions(**dims)
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model = whisper.model.Whisper(dims=dims)
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self.model = model
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self.encoder_output_size = self.model.dims.n_audio_state
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def forward(self, ):
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pass
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def inference(self,
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data_in,
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data_lengths=None,
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key: list = None,
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tokenizer=None,
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frontend=None,
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**kwargs,
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):
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if kwargs.get("batch_size", 1) > 1:
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raise NotImplementedError("batch decoding is not implemented")
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if frontend is None and not hasattr(self, "frontend"):
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frontend_class = tables.frontend_classes.get("WhisperFrontend")
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frontend = frontend_class(n_mels=self.model.dims.n_mels, do_pad_trim=kwargs.get("do_pad_trim", True))
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self.frontend = frontend
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else:
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frontend = frontend if frontend is not None else self.frontend
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meta_data = {}
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if isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank": # fbank
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speech, speech_lengths = data_in, data_lengths
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if len(speech.shape) < 3:
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speech = speech[None, :, :]
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if speech_lengths is None:
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speech_lengths = speech.shape[1]
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else:
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# extract fbank feats
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time1 = time.perf_counter()
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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),
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data_type=kwargs.get("data_type", "sound"),
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tokenizer=tokenizer)
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time2 = time.perf_counter()
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meta_data["load_data"] = f"{time2 - time1:0.3f}"
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speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
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frontend=frontend)
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time3 = time.perf_counter()
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meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
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frame_shift = frontend.frame_shift if hasattr(frontend, "frame_shift") else 10
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lfr_n = frontend.lfr_n if hasattr(frontend, "lfr_n") else 1
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meta_data["batch_data_time"] = speech_lengths.sum().item() * frame_shift * lfr_n / 1000
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speech = speech.to(device=kwargs["device"])[0, :, :]
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speech_lengths = speech_lengths.to(device=kwargs["device"])
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language = kwargs.get("language", None)
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initial_prompt = kwargs.get("initial_prompt", "<|startoftranscript|><|ASR|>")
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# # detect the spoken language
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# _, probs = self.model.detect_language(speech, initial_prompt=initial_prompt)
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# print(f"Detected language: {max(probs, key=probs.get)}")
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# language = max(probs, key=probs.get)
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# language = language if kwargs.get("language", None) is None else kwargs.get("language")
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# decode the audio
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prompt = ""
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initial_prompt = kwargs.get("initial_prompt", "<|startoftranscript|><|ASR|>")
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options = whisper.DecodingOptions(language=language, fp16=False, without_timestamps=True, initial_prompt=initial_prompt)
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result = whisper.decode(self.model, speech, options)
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results = []
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result_i = {"key": key[0], "text": result.text}
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results.append(result_i)
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return results, meta_data
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