This guide covers advanced audio capabilities beyond the basic TTS/ASR examples in the README. All of these are real, verified SDK features with no dedicated sample script, so examples here are built directly from the source signatures and their own docstrings.
VoiceEnrollmentService (CosyVoice v2) lets you register a custom voice from a short audio sample, then use it as a voice= value in SpeechSynthesizer calls:
from dashscope.audio.tts_v2 import VoiceEnrollmentService service = VoiceEnrollmentService() voice_id = service.create_voice( target_model="cosyvoice-v2", prefix="myvoice", # digits/lowercase letters only, <10 chars url="https://example.com/voice-sample.wav", ) print(voice_id) # List, inspect, or remove enrolled voices for voice in service.list_voices(prefix="myvoice"): print(voice) service.delete_voice(voice_id)
HotFix corrects mispronounced words or replaces text before synthesis (CosyVoice v2):
from dashscope.audio.tts_v2 import SpeechSynthesizer, HotFix hot_fix = HotFix( pronunciation=[{"草地": "cao3 di4"}], replace=[{"草地": "草弟"}], ) synthesizer = SpeechSynthesizer(model="cosyvoice-v2", voice="longxiaochun_v2", hot_fix=hot_fix)
TranslationRecognizerRealtime transcribes and translates streaming audio in one pass:
from dashscope.audio.asr import TranslationRecognizerCallback, TranslationRecognizerRealtime class Callback(TranslationRecognizerCallback): def on_event(self, request_id, transcription_result, translation_result, usage) -> None: if transcription_result is not None and transcription_result.is_sentence_end: print("transcript:", transcription_result.text) if translation_result is not None: translation = translation_result.get_translation("en") if translation.is_sentence_end: print("translation:", translation.text) translator = TranslationRecognizerRealtime( model="gummy-realtime-v1", format="pcm", sample_rate=16000, transcription_enabled=True, translation_enabled=True, translation_target_languages=["en"], callback=Callback(), ) translator.start() with open("audio.pcm", "rb") as f: while chunk := f.read(3200): translator.send_audio_frame(chunk) translator.stop()
AsrPhraseManager boosts recognition accuracy for specific words (e.g. song names, jargon), then applies them via phrase_id on Transcription.call:
from dashscope.audio.asr import AsrPhraseManager, Transcription # Values are boost weights, not counts job = AsrPhraseManager.create_phrases( model="paraformer-v1", phrases={"下一首": 90, "上一首": 90}, ) phrase_id = job.output.job_id response = Transcription.call( model=Transcription.Models.paraformer_v1, file_urls=["https://example.com/audio.wav"], phrase_id=phrase_id, )