import os
from pydub import AudioSegment
from smallestai.waves import WavesClient
client = WavesClient(api_key=os.getenv("SMALLEST_API_KEY"))
def preprocess_audio(input_path, output_path):
"""
Preprocess audio file to optimal format for Pulse STT:
- Convert to 16 kHz mono WAV
- Normalize audio levels
- Remove leading/trailing silence
"""
audio = AudioSegment.from_file(input_path)
audio = audio.set_frame_rate(16000).set_channels(1)
audio = audio.normalize()
audio = audio.strip_silence(silence_len=100, silence_thresh=-40)
audio.export(output_path, format="wav")
print(f"Preprocessed audio saved to: {output_path}")
return output_path
def transcribe_with_features(audio_path):
"""
Transcribe audio with age detection, emotion detection, and utterances.
"""
response = client.transcribe(
file_path=audio_path,
model="pulse",
language="en",
word_timestamps=True,
age_detection=True,
gender_detection=True,
emotion_detection=True,
diarize=True
)
return response
def process_results(response):
"""
Extract and display transcription results.
"""
print("=" * 60)
print("TRANSCRIPTION RESULTS")
print("=" * 60)
print(f"\nTranscription: {response.get('transcription', 'N/A')}")
if 'age' in response:
print(f"\nAge: {response['age']}")
if 'gender' in response:
print(f"Gender: {response['gender']}")
if 'emotions' in response:
print("\nEmotion Scores:")
emotions = response['emotions']
for emotion, score in emotions.items():
print(f" {emotion.capitalize()}: {score:.2f}")
if 'utterances' in response:
print("\nUtterances (Sentence-level timestamps):")
for i, utterance in enumerate(response['utterances'], 1):
speaker = utterance.get('speaker', 'unknown')
start = utterance.get('start', 0)
end = utterance.get('end', 0)
text = utterance.get('text', '')
print(f"\n [{i}] Speaker: {speaker}")
print(f" Time: {start:.2f}s - {end:.2f}s")
print(f" Text: {text}")
if 'words' in response:
print(f"\nWord-level timestamps: {len(response['words'])} words")
if __name__ == "__main__":
input_audio = "input_audio.mp3"
preprocessed_audio = "preprocessed_audio.wav"
try:
print("Preprocessing audio...")
preprocess_audio(input_audio, preprocessed_audio)
print("\nTranscribing audio with age, emotion, and utterance detection...")
result = transcribe_with_features(preprocessed_audio)
process_results(result)
if os.path.exists(preprocessed_audio):
os.remove(preprocessed_audio)
print("\nCleaned up temporary preprocessed file.")
except FileNotFoundError:
print(f"Error: Audio file '{input_audio}' not found.")
except Exception as e:
print(f"Error: {str(e)}")
