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feb23b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | import os
from huggingface_hub import snapshot_download
MODEL_CACHE_DIR = "./models"
SENSE_VOICE_LOCAL_PATH = os.path.join(MODEL_CACHE_DIR, "SenseVoiceSmall")
PARAFORMER_LOCAL_PATH = os.path.join(MODEL_CACHE_DIR, "paraformer-zh")
VAD_LOCAL_PATH = os.path.join(MODEL_CACHE_DIR, "fsmn-vad")
PUNC_LOCAL_PATH = os.path.join(MODEL_CACHE_DIR, "ct-punc")
os.makedirs(MODEL_CACHE_DIR, exist_ok=True)
def download_if_missing(repo_id, local_path, name):
if not os.path.exists(local_path):
print(f"Downloading {name}...")
snapshot_download(repo_id=repo_id, local_dir=local_path, ignore_patterns=["*.onnx"])
print(f"{name} ready.")
else:
print(f"{name} found locally.")
download_if_missing("FunAudioLLM/SenseVoiceSmall", SENSE_VOICE_LOCAL_PATH, "SenseVoice")
download_if_missing("funasr/paraformer-zh", PARAFORMER_LOCAL_PATH, "Paraformer-zh")
download_if_missing("funasr/fsmn-vad", VAD_LOCAL_PATH, "FSMN-VAD")
download_if_missing("funasr/ct-punc", PUNC_LOCAL_PATH, "CT-Punc")
import gradio as gr
import time
import tempfile
from funasr import AutoModel
from funasr.utils.postprocess_utils import rich_transcription_postprocess
loaded_models = {}
def get_model(pipeline):
if pipeline in loaded_models:
return loaded_models[pipeline]
if pipeline == "sensevoice":
model = AutoModel(
model=SENSE_VOICE_LOCAL_PATH,
vad_model=VAD_LOCAL_PATH,
vad_kwargs={"max_single_segment_time": 30000},
device="cpu",
disable_update=True,
hub="hf",
)
elif pipeline == "paraformer":
model = AutoModel(
model=PARAFORMER_LOCAL_PATH,
vad_model=VAD_LOCAL_PATH,
punc_model=PUNC_LOCAL_PATH,
device="cpu",
disable_update=True,
hub="hf",
)
else:
raise ValueError(f"Unknown pipeline: {pipeline}")
loaded_models[pipeline] = model
return model
def transcribe(audio_input, pipeline_type):
if audio_input is None:
return "Please upload or record audio.", ""
model = get_model(pipeline_type)
t0 = time.time()
if pipeline_type == "sensevoice":
res = model.generate(
input=audio_input, cache={}, language="auto",
use_itn=True, batch_size_s=60, merge_vad=True, merge_length_s=15,
)
else:
res = model.generate(input=audio_input)
text = rich_transcription_postprocess(res[0]["text"])
elapsed = time.time() - t0
metrics = f"Time: {elapsed:.2f}s | Model: {pipeline_type} | Device: CPU"
return metrics, text
with gr.Blocks(title="FunASR Demo") as demo:
gr.Markdown("""
# FunASR: Speech Recognition Demo
Industrial-grade ASR toolkit. Upload audio and get transcription instantly.
- **SenseVoice**: Multi-task (ASR + emotion + events), 5 languages, ultra-fast
- **Paraformer**: Non-autoregressive Chinese ASR with punctuation
[GitHub](https://github.com/modelscope/FunASR) | [Docs](https://modelscope.github.io/FunASR/) | [pip install funasr](https://pypi.org/project/funasr/)
""")
audio_input = gr.Audio(label="Upload or Record Audio", sources=["upload", "microphone"], type="filepath")
pipeline_type = gr.Dropdown(
choices=["sensevoice", "paraformer"],
label="Model",
value="sensevoice"
)
btn = gr.Button("Transcribe", variant="primary")
metrics_out = gr.Textbox(label="Metrics", lines=1)
text_out = gr.Textbox(label="Transcription", lines=8)
btn.click(transcribe, inputs=[audio_input, pipeline_type], outputs=[metrics_out, text_out])
gr.Markdown("""
### Install & Use Locally
```python
pip install funasr
from funasr import AutoModel
model = AutoModel(model="funasr/paraformer-zh", hub="hf", vad_model="funasr/fsmn-vad", punc_model="funasr/ct-punc")
result = model.generate(input="audio.wav")
```
""")
demo.queue().launch()
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