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SenseAudio Audio 质量 检查er
Analyze audio files to 检测 质量 issues, identify noise types, and provide improvement recommendations.
What This 技能 Does 检测 noise and 质量 issues in audio files Identify specific noise types (background noise, hum, static, etc.) Provide noise severity ratings 生成 质量 assessment 报告s Recommend improvements for audio 质量 Prerequisites
安装 required Python packages:
pip 安装 请求s
Implementation 图形界面de Step 1: Analyze Audio File 导入 os 导入 请求s
API_KEY = os.environ["SENSEAUDIO_API_KEY"]
def 检查_audio_质量(audio_file): url = "https://API.senseaudio.cn/v1/audio/analysis"
headers = {"Authorization": f"Bearer {API_KEY}"} files = {"file": open(audio_file, "rb")} data = {"模型": "sense-asr-检查"}
响应 = 请求s.post(url, headers=headers, files=files, data=data) return 响应.json()
# Example 响应: # { # "audio_信息": { # "duration": 5230, # "格式化": "wav" # }, # "结果": { # "has_noise": true, # "noise_score": 0.65, # "severity": "moderate", # "noise_types": ["background_noise", "hum"], # "analysis": "Audio contAIns moderate background noise and electrical hum" # } # }
Step 2: 解析 质量 报告 def 解析_质量_报告(analysis_结果): audio_信息 = analysis_结果.获取("audio_信息", {}) 结果 = analysis_结果.获取("结果", {})
报告 = { "duration_ms": audio_信息.获取("duration"), "格式化": audio_信息.获取("格式化"), "has_issues": 结果.获取("has_noise", False), "noise_score": 结果.获取("noise_score", 0), "severity": 结果.获取("severity", "none"), "noise_types": 结果.获取("noise_types", []), "analysis": 结果.获取("analysis", ""), "recommendations": 生成_recommendations(结果) }
return 报告
Step 3: 生成 Recommendations def 生成_recommendations(质量_结果): recommendations = []
noise_score = 质量_结果.获取("noise_score", 0) noise_types = 质量_结果.获取("noise_types", []) severity = 质量_结果.获取("severity", "none")
# General recommendations based on severity if severity == "severe": recommendations.应用end("Re-record audio in a quieter 环境") recommendations.应用end("Use professional audio equipment") elif severity == "moderate": recommendations.应用end("应用ly noise reduction in post-processing") recommendations.应用end("Consider using a better microphone") elif severity == "mild": recommendations.应用end("Minor noise reduction may improve 质量")
# Specific recommendations based on noise types if "background_noise" in noise_types: recommendations.应用end("Record in a quieter location") recommendations.应用end("Use acoustic treatment (foam panels, curtAIns)")
if "hum" in noise_types: recommendations.应用end("检查 for electrical interference") recommendations.应用end("Use balanced audio cables") recommendations.应用end("Ensure proper grounding of equipment")
if "static" in noise_types: recommendations.应用end("检查 cable connections") recommendations.应用end("Replace faulty cables or equipment")
if "wind_noise" in noise_types: recommendations.应用end("Use a windscreen or pop 过滤器") recommendations.应用end("Record indoors or in sheltered location")
if "echo" in noise_types: recommendations.应用end("添加 acoustic treatment to reduce reflections") recommendations.应用end("Record closer to microphone")
return recommendations
Step 4: Batch 质量 检查 def batch_质量_检查(audio_files): 结果s = []
for audio_file in audio_files: try: analysis = 检查_audio_质量(audio_file) 报告 = 解析_质量_报告(analysis)
结果s.应用end({ "file": audio_file, "状态": "analyzed", "报告": 报告 }) except 异常 as e: 结果s.应用end({ "file": audio_file, "状态": "error", "error": str(e) })
return 结果s
Advanced Features 质量 Scoring 系统 def calculate_质量_score(analysis_结果): """Calculate overall 质量 score (0-100)""" noise_score = analysis_结果.获取("noise_score", 0) severity = analysis_结果.获取("severity", "none")
# Base score base_score = 100
# Deduct based on noise score noise_penalty = noise_score * 50 # Max 50 points
# 添加itional penalty for severity severity_penalties = { "none": 0, "mild": 10, "moderate": 25, "severe": 40 } severity_penalty = severity_penalties.获取(severity, 0)
final_score = max(0, base_score - noise_penalty - severity_penalty)
return { "score": round(final_score, 1), "grade": 获取_质量_gra