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upload model and demo

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MIT License
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Copyright (c) 2025 FunAudioLLM
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from funasr import AutoModel
def main():
model_dir = "FunAudioLLM/fun-asr-nano"
model = AutoModel(
model=model_dir,
trust_remote_code=True,
remote_code="./model.py",
device="cuda:0",
)
wav_path = f"{model.model_path}/example/zh.mp3"
res = model.generate(input=[wav_path], cache={}, batch_size=1)
text = res[0]["text"]
print(text)
model = AutoModel(
model=model_dir,
trust_remote_code=True,
vad_model="fsmn-vad",
vad_kwargs={"max_single_segment_time": 30000},
remote_code="./model.py",
device="cuda:0",
)
res = model.generate(input=[wav_path], cache={}, batch_size=1)
text = res[0]["text"]
print(text)
if __name__ == "__main__":
main()

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from model import FunASRNano
def main():
model_dir = "FunAudioLLM/fun-asr-nano"
m, kwargs = FunASRNano.from_pretrained(model=model_dir, device="cuda:0")
m.eval()
wav_path = f"{kwargs['model_path']}/example/zh.mp3"
res = m.inference(data_in=[wav_path], **kwargs)
text = res[0][0]["text"]
print(text)
if __name__ == "__main__":
main()

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model.py Normal file
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import json
import logging
import os
import random
import re
import string
import time
import traceback
import torch
import torch.nn as nn
from funasr import AutoModel
from funasr.metrics.compute_acc import compute_accuracy
from funasr.register import tables
from funasr.train_utils.device_funcs import force_gatherable, to_device
from funasr.utils.datadir_writer import DatadirWriter
from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video
dtype_map = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}
@tables.register("model_classes", "FunASRNano")
class FunASRNano(nn.Module):
def __init__(
self,
audio_encoder: str = None,
audio_encoder_conf: dict = None,
audio_adaptor: str = None,
audio_adaptor_conf: dict = None,
llm: str = None,
llm_conf: dict = None,
input_size: int = 80,
length_normalized_loss: bool = False,
**kwargs,
):
super().__init__()
# audio encoder
hub = audio_encoder_conf.get("hub", None)
self.audio_encoder_activation_checkpoint = audio_encoder_conf.get(
"activation_checkpoint", False
)
if hub == "ms":
model = AutoModel(model=audio_encoder, model_revision="master")
audio_encoder_output_size = (
model.model.encoder_output_size
if hasattr(model.model, "encoder_output_size")
else -1
)
audio_encoder = (
model.model.model.encoder
if hasattr(model.model, "model")
else model.model.encoder
)
else:
encoder_class = tables.encoder_classes.get(audio_encoder)
audio_encoder = encoder_class(input_size=input_size, **audio_encoder_conf)
audio_encoder_output_size = audio_encoder.output_size()
freeze = audio_encoder_conf.get("freeze", True)
freeze_layer_num = int(audio_encoder_conf.get("freeze_layer_num", -1))
if freeze:
for name, param in audio_encoder.named_parameters():
param.requires_grad = False
audio_encoder.eval()
self.audio_encoder = audio_encoder
# llm
self.llm = None
init_param_path = llm_conf.get("init_param_path", None)
llm_dim = None
from transformers import AutoModelForCausalLM
llm_load_kwargs = llm_conf.get("load_kwargs", {})
model = AutoModelForCausalLM.from_pretrained(
init_param_path,
load_in_8bit=None,
device_map=None,
use_cache=None,
**llm_load_kwargs,
)
freeze = llm_conf.get("freeze", True)
if freeze:
for name, param in model.named_parameters():
param.requires_grad = False
model.eval()
logging.info(f"use_lora: {llm_conf.get('use_lora', False)}")
if llm_conf.get("use_lora", False):
from omegaconf import DictConfig, OmegaConf
lora_conf = llm_conf.get("lora_conf", {})
if isinstance(lora_conf, (OmegaConf, DictConfig)):
lora_conf = OmegaConf.to_container(lora_conf, resolve=True)
from peft import LoraConfig, PeftModel, get_peft_model
lora_init_param_path = lora_conf.get("init_param_path", None)
if lora_init_param_path is not None:
logging.info(f"lora_init_param_path: {lora_init_param_path}")
model = PeftModel.from_pretrained(model, lora_init_param_path)
for name, param in model.named_parameters():
if not lora_conf.get("freeze_lora", False):
if "lora_" in name:
param.requires_grad = True
else:
peft_config = LoraConfig(**lora_conf)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
if llm_conf.get("activation_checkpoint", False):
model.gradient_checkpointing_enable()
self.llm_dtype = llm_conf.get("llm_dtype", "fp32")
self.llm = model.to(dtype_map[self.llm_dtype])
llm_dim = model.get_input_embeddings().weight.shape[-1]
# adaptor
adaptor_class = tables.adaptor_classes.get(audio_adaptor)
if audio_encoder_output_size > 0:
audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size
audio_adaptor_conf["llm_dim"] = (
llm_dim if llm_dim is not None else audio_adaptor_conf["llm_dim"]
)
audio_adaptor = adaptor_class(**audio_adaptor_conf)
init_param_path = audio_adaptor_conf.get("init_param_path", None)
if init_param_path is not None:
src_state = torch.load(init_param_path, map_location="cpu")
flag = audio_adaptor.load_state_dict(src_state, strict=False)
logging.info(
f"Loading audio_adaptor ckpt: {init_param_path}, status: {flag}"
)
freeze = audio_adaptor_conf.get("freeze", False)
if freeze:
for name, param in audio_adaptor.named_parameters():
param.requires_grad = False
audio_adaptor.eval()
self.audio_adaptor = audio_adaptor
self.length_normalized_loss = length_normalized_loss
self.feat_permute = audio_encoder_conf.get("feat_permute", True)
rank = int(os.environ.get("RANK", 0))
logging.info(f"rank: {rank}, model is builded.")
def forward(
self,
speech: torch.Tensor = None,
speech_lengths: torch.Tensor = None,
input_ids: torch.Tensor = None,
attention_mask: torch.Tensor = None,
labels_ids: torch.Tensor = None,
fbank_beg: torch.Tensor = None,
fbank_mask: torch.Tensor = None,
**kwargs,
):
batch_size, token_num = input_ids.shape
stats = {}
input_ids[input_ids < 0] = 0
inputs_embeds = self.llm.model.get_input_embeddings()(input_ids)
if speech is not None:
if len(speech_lengths.size()) > 1:
speech_lengths = speech_lengths[:, 0]
batch_size_speech, frames, _ = speech.shape
# audio encoder
if self.audio_encoder_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)
# audio_adaptor
encoder_out, encoder_out_lens = self.audio_adaptor(
encoder_out, encoder_out_lens
)
batch_size, token_num, dims = inputs_embeds.shape
fake_token_len = kwargs.get("fake_token_len")
fake_token_len[fake_token_len < 0] = 0
fbank_beg[fbank_beg < 0] = 0
speech_idx = 0
for batch_idx in range(batch_size):
for turn_id in range(fbank_beg.shape[1]):
fbank_beg_idx = fbank_beg[batch_idx, turn_id].item()
if fbank_beg_idx > 0:
speech_token_len = fake_token_len[batch_idx, turn_id]
speech_token = encoder_out[speech_idx, :speech_token_len, :]
try:
inputs_embeds[
batch_idx,
fbank_beg_idx : fbank_beg_idx + speech_token_len,
:,
] = speech_token
except Exception as e:
logging.error(f"{str(e)}, {traceback.format_exc()}")
logging.info(
f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}"
)
speech_token_len = encoder_out_lens[speech_idx].item()
speech_token = encoder_out[speech_idx, :speech_token_len, :]
inputs_embeds[
batch_idx,
fbank_beg_idx : fbank_beg_idx + speech_token_len,
:,
] = speech_token
speech_idx += 1
stats["batch_size_speech"] = batch_size_speech
stats["batch_size_x_frames"] = frames * batch_size_speech
stats["batch_size_real_frames"] = speech_lengths.sum().item()
stats["padding_frames"] = (
stats["batch_size_x_frames"] - stats["batch_size_real_frames"]
)
with torch.cuda.amp.autocast(
enabled=True if self.llm_dtype != "fp32" else False,
dtype=dtype_map[self.llm_dtype],
):
labels_ids[labels_ids == -1] = -100
attention_mask[attention_mask < 0] = 0
model_outputs = self.llm(
inputs_embeds=inputs_embeds.to(dtype_map[self.llm_dtype]),
attention_mask=attention_mask,
labels=labels_ids,
)
loss = model_outputs.loss
with torch.no_grad():
preds = torch.argmax(model_outputs.logits, -1)
acc_att = compute_accuracy(
preds[:, :-1], labels_ids[:, 1:], ignore_label=-100
)
stats["acc"] = acc_att
stats["loss"] = torch.clone(loss.detach())
stats["batch_size"] = batch_size
stats["batch_size_x_tokens"] = token_num * batch_size
stats["batch_size_real_tokens"] = attention_mask.sum().item()
stats["padding_tokens"] = (
stats["batch_size_x_tokens"] - stats["batch_size_real_tokens"]
)
dialog_turns = (fbank_beg > 0).sum(-1)
dialog_turns_max = torch.max(dialog_turns).int().item()
dialog_turns_avg = dialog_turns.sum().item() / batch_size
stats["dialog_turns_max"] = dialog_turns_max
stats["dialog_turns_avg"] = dialog_turns_avg
# force_gatherable: to-device and to-tensor if scalar for DataParallel
if self.length_normalized_loss:
batch_size = int((labels_ids > 0 + 1).sum())
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
def forward_export(self, speech, speech_lengths, **kwargs):
x, olens = self.audio_encoder(speech, speech_lengths)
encoder_out, encoder_out_lens = self.audio_adaptor(x, olens)
return encoder_out, encoder_out_lens
def encode(self, speech, speech_lengths):
# audio encoder
if self.feat_permute:
encoder_out, encoder_out_lens = self.audio_encoder(
speech.permute(0, 2, 1), speech_lengths
)
else:
encoder_out, encoder_out_lens = self.audio_encoder(speech, speech_lengths)
return encoder_out, encoder_out_lens
def data_template(self, data):
system, user, assistant = [], [], []
for i, item in enumerate(data):
role = item["role"]
content = item["content"]
if role == "system":
system.append(content)
elif role == "user":
if "audio" in item:
audio = item["audio"]
content = [content, audio]
user.append(content)
elif role == "assistant":
assistant.append(content)
system = system * len(user)
contents = {
"system": system,
"user": user,
"assistant": assistant,
}
return contents
def data_load_speech(
self, contents: dict, tokenizer, frontend, meta_data={}, **kwargs
):
system = contents["system"]
user = contents["user"]
assistant = contents["assistant"]
pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)")
do_think = True
sys_prompt = True
if "dataset_conf" in kwargs:
do_think = kwargs["dataset_conf"].get("do_think", True)
sys_prompt = kwargs["dataset_conf"].get("sys_prompt", True)
input_ids, labels, fbank, fbank_lens, fbank_mask, fbank_beg, fake_token_len = (
[],
[],
[],
[],
[],
[],
[],
)
input_source_ids = []
for i, (system_prompt, user_prompt, target_out) in enumerate(
zip(system, user, assistant)
):
if i >= kwargs.get("multiturn_num_max", 5):
break
if len(input_ids) > kwargs.get("max_token_length", 1500):
break
if isinstance(user_prompt, (list, tuple)):
user_prompt, audio = user_prompt
if i == 0:
if kwargs.get("infer_with_assistant_input", False):
source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}"
if not sys_prompt:
source_input = f"<|im_start|>user\n{user_prompt}"
else:
source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"
if not sys_prompt:
source_input = f"<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"
else:
if kwargs.get("infer_with_assistant_input", False):
source_input = f"<|im_start|>user\n{user_prompt}"
else:
source_input = f"<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"
if not do_think:
source_input += "<think>\n\n</think>\n\n"
splits = pattern.split(source_input)
source_ids = []
fbank_mask_i = []
fake_token_len_i = 0
fbank_beg_i = -1
speech, speech_lengths = [], []
for k, sub_str in enumerate(splits):
if not sub_str.startswith("<|startofspeech|>"):
sub_token = tokenizer.encode(sub_str)
source_ids += sub_token
fbank_mask_i += [0] * len(sub_token)
else:
sub_str = sub_str.replace("<|startofspeech|>", "").replace(
"<|endofspeech|>", ""
)
if sub_str.startswith("!"):
sub_str = sub_str[1:]
if sub_str.startswith("!"): # !!: audio sample point
sub_str = audio
try:
time1 = time.perf_counter()
data_src = load_audio_text_image_video(
sub_str, fs=frontend.fs, **kwargs
)
time2 = time.perf_counter()
meta_data["load_data"] = f"{time2 - time1:0.3f}"
except Exception as e:
logging.error(
f"Loading wav failed! {str(e)}, {traceback.format_exc()}"
)
speech, speech_lengths = extract_fbank(
data_src,
data_type=kwargs.get("data_type", "sound"),
frontend=frontend,
is_final=True,
) # speech: [b, T, d]
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
)
if self.feat_permute:
speech = speech.permute(0, 2, 1)
olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2
olens = 1 + (olens - 3 + 2 * 1) // 2
fake_token_len_i = (olens - 1) // 2 + 1
fake_token = [0] * fake_token_len_i
fbank_beg_i = len(source_ids)
source_ids += fake_token
fbank_mask_i += [1] * len(fake_token)
fbank_beg += [fbank_beg_i + len(input_ids)]
fake_token_len += [fake_token_len_i]
source_mask = [-100] * len(source_ids)
target_out = f"{target_out}<|im_end|>"
target_ids = tokenizer.encode(target_out)
input_source_ids = input_ids + source_ids
input_ids += source_ids + target_ids
labels += source_mask + target_ids
fbank_mask += fbank_mask_i
if len(speech) > 0:
fbank.append(speech[0, :, :])
fbank_lens.append(speech_lengths)
input_ids = torch.tensor(
input_ids, dtype=torch.int64
) # [: self.max_token_length]
attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32)
labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length]
fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32)
fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32)
fake_token_len = torch.tensor(fake_token_len, dtype=torch.int32)
source_ids = torch.tensor(input_source_ids, dtype=torch.int64)
target_ids = torch.tensor(target_ids, dtype=torch.int64)
if len(fbank) > 0:
speech = torch.nn.utils.rnn.pad_sequence(
fbank, batch_first=True, padding_value=0.0
)
speech_lengths = torch.nn.utils.rnn.pad_sequence(
fbank_lens, batch_first=True, padding_value=-1
)
else:
speech = []
speech_lengths = []
output = {
"speech": speech,
"speech_lengths": speech_lengths,
"fbank_mask": fbank_mask[None, :],
"fbank_beg": fbank_beg[None,],
"fake_token_len": fake_token_len[None, :],
"input_ids": input_ids[None,],
"attention_mask": attention_mask[None,],
"labels_ids": labels,
"source_ids": source_ids[None, :],
"target_ids": target_ids[None, :],
}
return output
def inference_prepare(
self,
data_in,
data_lengths=None,
key: list = None,
tokenizer=None,
frontend=None,
**kwargs,
):
meta_data = {}
if kwargs.get("batch_size", 1) > 1:
raise NotImplementedError("batch decoding is not implemented")
contents = self.data_template(data_in[0])
output = self.data_load_speech(
contents, tokenizer, frontend, meta_data=meta_data, **kwargs
)
batch = to_device(output, kwargs["device"])
# audio encoder
speech = batch["speech"]
if len(speech) > 0:
if "audio_embedding" in kwargs and "audio_embedding_lens" in kwargs:
encoder_out = kwargs["audio_embedding"]
encoder_out_lens = kwargs["audio_embedding_lens"]
else:
speech_lengths = batch["speech_lengths"][:, 0]
# fp16
if kwargs.get("fp16", False):
speech = speech.to(torch.float16)
elif kwargs.get("bf16", False):
speech = speech.to(torch.bfloat16)
# audio encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
# audio_adaptor
encoder_out, encoder_out_lens = self.audio_adaptor(
encoder_out, encoder_out_lens
)
meta_data["audio_adaptor_out"] = encoder_out
meta_data["audio_adaptor_out_lens"] = encoder_out_lens
input_ids = batch["input_ids"]
source_ids = batch["source_ids"]
fbank_beg = batch["fbank_beg"]
fake_token_len = batch["fake_token_len"]
if not kwargs.get("tearchforing", False):
input_ids = source_ids
input_ids[input_ids < 0] = 0
inputs_embeds = self.llm.model.get_input_embeddings()(input_ids)
batch_size, token_num, dims = inputs_embeds.shape
fake_token_len[fake_token_len < 0] = 0
fbank_beg[fbank_beg < 0] = 0
speech_idx = 0
for batch_idx in range(batch_size):
for turn_id in range(fbank_beg.shape[1]):
fbank_beg_idx = fbank_beg[batch_idx, turn_id].item()
if fbank_beg_idx > 0:
speech_token_len = fake_token_len[batch_idx, turn_id]
speech_token = encoder_out[speech_idx, :speech_token_len, :]
try:
inputs_embeds[
batch_idx,
fbank_beg_idx : fbank_beg_idx + speech_token_len,
:,
] = speech_token
except Exception as e:
#
logging.error(f"{str(e)}, {traceback.format_exc()}")
logging.info(
f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}"
)
speech_token_len = encoder_out_lens[speech_idx].item()
speech_token = encoder_out[speech_idx, :speech_token_len, :]
inputs_embeds[
batch_idx,
fbank_beg_idx : fbank_beg_idx + speech_token_len,
:,
] = speech_token
speech_idx += 1
return inputs_embeds, contents, batch, source_ids, meta_data
def inference(
self,
data_in,
data_lengths=None,
key: list = None,
tokenizer=None,
frontend=None,
**kwargs,
):
new_data_in = []
for data in data_in:
if isinstance(data, str):
new_data_in.append(
[
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": f"语音转写：<|startofspeech|>!{data}<|endofspeech|>",
},
{"role": "assistant", "content": "null"},
]
)
elif isinstance(data, torch.Tensor):
new_data_in.append(
[
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": f"语音转写：<|startofspeech|>!!<|endofspeech|>",
"audio": data,
},
{"role": "assistant", "content": "null"},
]
)
data_in = new_data_in
if key is None:
key = []
for _ in data_in:
chars = string.ascii_letters + string.digits
key.append(
"rand_key_" + "".join(random.choice(chars) for _ in range(13))
)
return self.inference_llm(
data_in,
data_lengths=data_lengths,
key=key,
tokenizer=tokenizer,
frontend=frontend,
**kwargs,
)
def inference_llm(
self,
data_in,
data_lengths=None,
key: list = None,
tokenizer=None,
frontend=None,
**kwargs,
):
inputs_embeds, contents, batch, source_ids, meta_data = self.inference_prepare(
data_in, data_lengths, key, tokenizer, frontend, **kwargs
)
llm_dtype = kwargs.get("llm_dtype", "fp32")
if llm_dtype == "fp32":
llm_dtype = "fp16" if kwargs.get("fp16", False) else llm_dtype
llm_dtype = "bf16" if kwargs.get("bf16", False) else llm_dtype
with torch.cuda.amp.autocast(
enabled=True if llm_dtype != "fp32" else False, dtype=dtype_map[llm_dtype]
):
label = contents["assistant"][-1]
self.llm = self.llm.to(dtype_map[llm_dtype])
inputs_embeds = inputs_embeds.to(dtype_map[llm_dtype])
llm_kwargs = kwargs.get("llm_kwargs", {})
if not kwargs.get("teachforing", False):
generated_ids = self.llm.generate(
inputs_embeds=inputs_embeds,
max_new_tokens=kwargs.get("max_length", 512),
**llm_kwargs,
)
response = tokenizer.batch_decode(
generated_ids,
skip_special_tokens=kwargs.get("skip_special_tokens", True),
)[0]
loss = None
else:
labels_ids = batch["labels_ids"]
labels_ids[labels_ids == -1] = -100
attention_mask = batch.get("attention_mask", None)
model_outputs = self.llm(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
labels=labels_ids,
**llm_kwargs,
)
preds = torch.argmax(model_outputs.logits, -1)[:, source_ids.shape[1] :]
response = tokenizer.batch_decode(
preds,
add_special_tokens=False,
skip_special_tokens=kwargs.get("skip_special_tokens", True),
)[0]
loss = model_outputs.loss.item()
ibest_writer = None
if kwargs.get("output_dir") is not None:
if not hasattr(self, "writer"):
self.writer = DatadirWriter(kwargs.get("output_dir"))
ibest_writer = self.writer[f"{0 + 1}best_recog"]
results = []
response_clean = re.sub(r"[^\w\s\u3000\u4e00-\u9fff]+", "", response)
result_i = {
"key": key[0],
"text": response,
"text_tn": response_clean,
"label": label,
}
if loss is not None:
result_i["loss"] = loss
results.append(result_i)
if ibest_writer is not None:
ibest_writer["text"][key[0]] = response.replace("\n", " ")
ibest_writer["label"][key[0]] = label.replace("\n", " ")
ibest_writer["text_tn"][key[0]] = response_clean
return results, meta_data
@staticmethod
def from_pretrained(model: str = None, **kwargs):
from funasr import AutoModel
model, kwargs = AutoModel.build_model(
model=model, trust_remote_code=True, **kwargs
)
return model, kwargs

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