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import os
import pytest
import torch
import whisper
from whisper.tokenizer import get_tokenizer
@pytest.mark.parametrize("model_name", whisper.available_models())
def test_transcribe(model_name: str):
device = "cuda" if torch.cuda.is_available() else "cpu"
model = whisper.load_model(model_name).to(device)
audio_path = os.path.join(os.path.dirname(__file__), "jfk.flac")
language = "en" if model_name.endswith(".en") else None
result = model.transcribe(
audio_path, language=language, temperature=0.0, word_timestamps=True
)
assert result["language"] == "en"
assert result["text"] == "".join([s["text"] for s in result["segments"]])
transcription = result["text"].lower()
assert "my fellow americans" in transcription
assert "your country" in transcription
assert "do for you" in transcription
tokenizer = get_tokenizer(model.is_multilingual)
all_tokens = [t for s in result["segments"] for t in s["tokens"]]
assert tokenizer.decode(all_tokens) == result["text"]
assert tokenizer.decode_with_timestamps(all_tokens).startswith("<|0.00|>")
timing_checked = False
for segment in result["segments"]:
for timing in segment["words"]:
assert timing["start"] < timing["end"]
if timing["word"].strip(" ,") == "Americans":
assert timing["start"] <= 1.8
assert timing["end"] >= 1.8
timing_checked = True
assert timing_checked
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