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import io
import threading
import time
import os
import numpy as np
import torch
import torchaudio
import onnxruntime
import whisper
from funasr_detach import AutoModel
from utils import resample_audio, energy_norm_fn, trim_silence
class StepAudioTokenizer:
def __init__(
self,
encoder_path,
):
funasr_model_path = os.path.join(
encoder_path,
"dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online",
)
kms_path = os.path.join(encoder_path, "km_iter_3000.npy")
cosy_tokenizer_path = os.path.join(encoder_path, "speech_tokenizer_v1.onnx")
self.funasr_model = AutoModel(model=funasr_model_path, model_revision="master")
self.kms = torch.tensor(np.load(kms_path))
providers = ["CUDAExecutionProvider"]
session_option = onnxruntime.SessionOptions()
session_option.graph_optimization_level = (
onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
)
session_option.intra_op_num_threads = 1
self.ort_session = onnxruntime.InferenceSession(
cosy_tokenizer_path, sess_options=session_option, providers=providers
)
self.chunk_size = [0, 4, 5]
self.encoder_chunk_look_back = 4
self.decoder_chunk_look_back = 1
self.vq02_sessions = {}
self.vq02_lock = threading.Lock()
self.vq06_lock = threading.Lock()
def __call__(self, audio, sr):
_, vq02, vq06 = self.wav2token(audio, sr, False)
text = self.merge_vq0206_to_token_str(vq02, vq06)
return text
def preprocess_wav(self, audio, sample_rate, enable_trim=True, energy_norm=True):
audio = resample_audio(audio, sample_rate, 16000)
if energy_norm:
audio = energy_norm_fn(audio)
if enable_trim:
audio = audio.cpu().numpy().squeeze(0)
audio = trim_silence(audio, 16000)
audio = torch.from_numpy(audio)
audio = audio.unsqueeze(0)
return audio
def wav2token(self, audio, sample_rate, enable_trim=True, energy_norm=True):
audio = self.preprocess_wav(
audio, sample_rate, enable_trim=enable_trim, energy_norm=energy_norm
)
vq02_ori = self.get_vq02_code(audio)
vq02 = [int(x) + 65536 for x in vq02_ori]
vq06_ori = self.get_vq06_code(audio)
vq06 = [int(x) + 65536 + 1024 for x in vq06_ori]
chunk = 1
chunk_nums = min(len(vq06) // (3 * chunk), len(vq02) // (2 * chunk))
speech_tokens = []
for idx in range(chunk_nums):
speech_tokens += vq02[idx * chunk * 2 : (idx + 1) * chunk * 2]
speech_tokens += vq06[idx * chunk * 3 : (idx + 1) * chunk * 3]
return speech_tokens, vq02_ori, vq06_ori
def get_vq02_code(self, audio, session_id=None, is_final=True):
_tmp_wav = io.BytesIO()
torchaudio.save(_tmp_wav, audio, 16000, format="wav")
_tmp_wav.seek(0)
with self.vq02_lock:
cache = {}
if session_id in self.vq02_sessions:
cache = self.vq02_sessions[session_id].get("cache", {})
res, new_cache = self.funasr_model.infer_encoder(
input=[_tmp_wav],
chunk_size=self.chunk_size,
encoder_chunk_look_back=self.encoder_chunk_look_back,
decoder_chunk_look_back=self.decoder_chunk_look_back,
device=0,
is_final=is_final,
cache=cache,
)
c_list = []
for j, res_ in enumerate(res):
feat = res_["enc_out"]
if len(feat) > 0:
c_list = self.dump_label([feat], self.kms)[0]
if is_final:
if session_id in self.vq02_sessions:
self.vq02_sessions.pop(session_id)
else:
if isinstance(session_id, str) and len(session_id) > 0:
self.vq02_sessions[session_id] = {
"cache": new_cache,
"update_time": time.time(),
}
return c_list
def get_vq06_code(self, audio):
def split_audio(audio, chunk_duration=480000):
start = 0
chunks = []
while start < len(audio):
end = min(start + chunk_duration, len(audio))
chunk = audio[start:end]
if len(chunk) < 480:
pass
else:
chunks.append(chunk)
start = end
return chunks
with self.vq06_lock:
audio = audio.squeeze(0)
chunk_audios = split_audio(audio, chunk_duration=30 * 16000) # 最大支持30s
speech_tokens = []
for chunk in chunk_audios:
duration = round(chunk.shape[0] / 16000, 2)
feat = whisper.log_mel_spectrogram(chunk, n_mels=128)
feat = feat.unsqueeze(0)
feat_len = np.array([feat.shape[2]], dtype=np.int32)
chunk_token = (
self.ort_session.run(
None,
{
self.ort_session.get_inputs()[0]
.name: feat.detach()
.cpu()
.numpy(),
self.ort_session.get_inputs()[1].name: feat_len,
},
)[0]
.flatten()
.tolist()
)
assert abs(len(chunk_token) - duration * 25) <= 2
speech_tokens += chunk_token
return speech_tokens
def kmean_cluster(self, samples, means):
dists = torch.cdist(samples, means)
indices = dists.argmin(dim=1).cpu().numpy()
return indices.tolist()
def dump_label(self, samples, mean):
dims = samples[0].shape[-1]
x_lens = [x.shape[1] for x in samples]
total_len = sum(x_lens)
x_sel = torch.FloatTensor(1, total_len, dims)
start_len = 0
for sample in samples:
sample_len = sample.shape[1]
end_len = start_len + sample_len
x_sel[:, start_len:end_len] = sample
start_len = end_len
dense_x = x_sel.squeeze(0)
indices = self.kmean_cluster(dense_x, mean)
indices_list = []
start_len = 0
for x_len in x_lens:
end_len = start_len + end_len
indices_list.append(indices[start_len:end_len])
return indices_list
def merge_vq0206_to_token_str(self, vq02, vq06):
_vq06 = [1024 + x for x in vq06]
result = []
i = 0
j = 0
while i < len(vq02) - 1 and j < len(_vq06) - 2:
sublist = vq02[i : i + 2] + _vq06[j : j + 3]
result.extend(sublist)
i += 2
j += 3
return "".join([f"<audio_{x}>" for x in result])
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