Spaces:
Runtime error
Runtime error
File size: 3,193 Bytes
889a5cc 14b80ce 889a5cc 14b80ce 56bdf87 889a5cc 47af204 889a5cc 15ad32c 47af204 889a5cc 47af204 15ad32c 889a5cc e8db4c4 14b80ce e8db4c4 889a5cc 47af204 14b80ce 47af204 48b5bc5 1f7df9f 56bdf87 14b80ce |
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 |
from funasr import AutoModel
from funasr.utils.postprocess_utils import rich_transcription_postprocess
from modelscope import snapshot_download
import io
import os
import tempfile
import json
from typing import Optional
import torch
import gradio as gr
from config import model_config
from gradio_client import Client, handle_file
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model_dir = snapshot_download(model_config['model_dir'])
model = AutoModel(
model=model_dir,
trust_remote_code=False,
remote_code="./model.py",
vad_model="fsmn-vad",
vad_kwargs={"max_single_segment_time": 30000},
ncpu=4,
batch_size=1,
hub="ms",
device=device,
)
def transcribe_audio(file_path, vad_model="fsmn-vad", vad_kwargs='{"max_single_segment_time": 30000}',
ncpu=4, batch_size=1, language="auto", use_itn=True, batch_size_s=60,
merge_vad=True, merge_length_s=15, batch_size_threshold_s=50,
hotword=" ", spk_model="cam++", ban_emo_unk=False):
try:
vad_kwargs = json.loads(vad_kwargs)
temp_file_path = file_path
res = model.generate(
input=temp_file_path,
cache={},
language=language,
use_itn=use_itn,
batch_size_s=batch_size_s,
merge_vad=merge_vad,
merge_length_s=merge_length_s,
batch_size_threshold_s=batch_size_threshold_s,
hotword=hotword,
spk_model=spk_model,
ban_emo_unk=ban_emo_unk
)
text = rich_transcription_postprocess(res[0]["text"])
return text
except Exception as e:
return str(e)
inputs = [
gr.Audio(type="filepath"),
gr.Textbox(value="fsmn-vad", label="VAD Model"),
gr.Textbox(value='{"max_single_segment_time": 30000}', label="VAD Kwargs"),
gr.Slider(1, 8, value=4, step=1, label="NCPU"),
gr.Slider(1, 10, value=1, step=1, label="Batch Size"),
gr.Textbox(value="auto", label="Language"),
gr.Checkbox(value=True, label="Use ITN"),
gr.Slider(30, 120, value=60, step=1, label="Batch Size (seconds)"),
gr.Checkbox(value=True, label="Merge VAD"),
gr.Slider(5, 60, value=15, step=1, label="Merge Length (seconds)"),
gr.Slider(10, 100, value=50, step=1, label="Batch Size Threshold (seconds)"),
gr.Textbox(value=" ", label="Hotword"),
gr.Textbox(value="cam++", label="Speaker Model"),
gr.Checkbox(value=False, label="Ban Emotional Unknown"),
]
outputs = gr.Textbox(label="Transcription")
gr.Interface(
fn=transcribe_audio,
inputs=inputs,
outputs=outputs,
title="ASR Transcription with FunASR"
).launch()
client = Client("TaiYouWeb/funasr-svsmall-cpu")
result = client.predict(
file_path=handle_file('https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav'),
vad_model="fsmn-vad",
vad_kwargs="{"max_single_segment_time": 30000}",
ncpu=4,
batch_size=1,
language="auto",
use_itn=True,
batch_size_s=60,
merge_vad=True,
merge_length_s=15,
batch_size_threshold_s=50,
hotword=" ",
spk_model="cam++",
ban_emo_unk=False,
api_name="/asr"
)
|