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"""Optional local Whisper transcription of reference audio."""
import logging
from typing import Optional
logger = logging.getLogger(__name__)
def transcribe_reference_audio(audio_path: str, model_name: str = "base") -> Optional[str]:
"""Transcribe reference audio locally using an optional Whisper backend.
The current qwen-tts demo does not expose a transcription endpoint, so
transcription is done client-side when a Whisper package is available.
Returns None if no backend is installed.
"""
for backend in ("faster_whisper", "whisper"):
try:
if backend == "faster_whisper":
from faster_whisper import WhisperModel
model = WhisperModel(model_name, device="cpu", compute_type="int8")
segments, _ = model.transcribe(audio_path)
text = " ".join(seg.text.strip() for seg in segments).strip()
else:
import whisper
model = whisper.load_model(model_name)
result = model.transcribe(audio_path)
text = (result.get("text") or "").strip()
if text:
logger.info("Transcription complete via %s: %s", backend, text)
return text
except ImportError:
continue
except Exception as exc:
logger.warning("%s transcription failed: %s", backend, exc)
logger.warning("No Whisper backend available; transcription skipped.")
return None
def whisper_backend_available() -> Optional[str]:
"""Return the name of an importable Whisper backend, or None.
Checks faster_whisper first (preferred), then the openai-whisper
package, without importing the heavy model code: a bare import probe
is enough to tell whether the package is installed in the current
environment. Used by the make_audiocpp_server_json tool to warn when
neither is present (e.g. the wrong conda environment is active).
"""
for backend in ("faster_whisper", "whisper"):
try:
__import__(backend)
except ImportError:
continue
return backend
return None
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