

Now this result looks more accurate! For a deep-dive comparison on Wav2Vec2 vs Whisper, refer to the Audio Transformers Course.
We really encourage you to check out the Hub for models in different languages, models specialized in your field, and more.
You can check out and compare model results directly from your browser on the Hub to see if it fits or 
handles corner cases better than other ones.
And if you don't find a model for your use case, you can always start training your own!
If you have several inputs, you can pass your input as a list:
py
transcriber(
    [
        "https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac",
        "https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac",
    ]
)
Pipelines are great for experimentation as switching from one model to another is trivial; however, there are some ways to optimize them for larger workloads than experimentation. See the following guides that dive into iterating over whole datasets or using pipelines in a webserver:
of the docs:
* Using pipelines on a dataset
* Using pipelines for a webserver
Parameters
[pipeline] supports many parameters; some are task specific, and some are general to all pipelines.
In general, you can specify parameters anywhere you want: