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import os
import warnings
import whisper
import librosa
import numpy as np
import tensorflow_hub as hub
# Reduce TensorFlow log noise and avoid attempting GPU / oneDNN on CPU-only envs
os.environ.setdefault("TF_CPP_MIN_LOG_LEVEL", "2") # hide INFO/WARNING logs
os.environ.setdefault("TF_ENABLE_ONEDNN_OPTS", "0") # disable oneDNN custom ops
os.environ.setdefault("CUDA_VISIBLE_DEVICES", "-1") # don't try to use CUDA GPUs
# Suppress specific library warnings that are expected in this setup
warnings.filterwarnings(
"ignore",
category=UserWarning,
message="FP16 is not supported on CPU; using FP32 instead",
)
warnings.filterwarnings(
"ignore",
category=FutureWarning,
module="librosa",
)
# Load ASR
asr_model = whisper.load_model("small")
# Load YAMNet for sound classification
yamnet = hub.load("https://tfhub.dev/google/yamnet/1")
class_map = yamnet.class_map_path().numpy()
# Simple Emotion Estimator (from YAMNet embedding)
def estimate_emotion(activation):
mean_val = activation.mean()
if mean_val > 0.3:
return "Happy / Excited"
elif mean_val < -0.3:
return "Sad / Depressed"
return "Neutral"
def speech_to_text(audio):
# Force FP32 on CPU to avoid FP16 warnings and ensure compatibility
result = asr_model.transcribe(audio, fp16=False)
return result["text"]
def detect_sound(audio):
# Load mono waveform at 16 kHz as 1D float32 array, as expected by YAMNet
waveform, sr = librosa.load(audio, sr=16000, mono=True)
waveform = waveform.astype(np.float32)
scores, embeddings, _ = yamnet(waveform)
mean_scores = np.mean(scores.numpy(), axis=0)
top_idx = int(np.argmax(mean_scores))
# class_map may contain integers or byte strings depending on TF Hub version;
# convert robustly to a human-readable label.
label = class_map[top_idx]
if isinstance(label, bytes):
label = label.decode("utf-8")
else:
label = str(label)
return label, float(mean_scores.max())
def analyze_audio(audio_file):
summary = {}
summary["transcription"] = speech_to_text(audio_file)
event, confidence = detect_sound(audio_file)
summary["sound_event"] = event
summary["sound_confidence"] = float(confidence)
summary["emotion"] = "Neutral (approx)"
summary["speakers"] = "Not available in HF-free version"
return summary
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