SAVRN Model Hub · Comparisons
mms-300m-1130-forced-aligner vs wav2vec2-xls-r-300m-ftspeech
Mms-300m-1130-forced-aligner has 315M parameters and wav2vec2-xls-r-300m-ftspeech has 315M parameters; mms-300m-1130-forced-aligner is released under Creative Commons Attribution-NonCommercial 4.0 and wav2vec2-xls-r-300m-ftspeech under other; at 16-bit, mms-300m-1130-forced-aligner needs about 0.8 GB (1x MI300X from $1.85 an hour) and wav2vec2-xls-r-300m-ftspeech about 0.8 GB (1x MI300X from $1.85 an hour).
| Field | mms-300m-1130-forced-aligner MahmoudAshraf/mms-300m-1130-forced-aligner | wav2vec2-xls-r-300m-ftspeech saattrupdan/wav2vec2-xls-r-300m-ftspeech |
|---|---|---|
| Publisher | Mahmoud Ashraf | Dan Saattrup Smart |
| Task | Speech recognition | Speech recognition |
| Modality | Audio | Audio |
| Parameters, as reported | 315M parameters | 315M parameters |
| Architecture | Wav2Vec2ForCTC | Wav2Vec2ForCTC |
| Library | transformers | transformers |
| Context length | Not stated | Not stated |
| Repository size | 2.5 GB | 3.8 GB |
| Artifact formats | safetensors, pytorch | safetensors, pytorch |
| License | cc-by-nc-4.0 | other |
| Access | Open weights, no gate | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 0.8 GB | 0.8 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 0.2 GB | 0.2 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 49402e9577b1 | 7a60985d596e |
| Downloads reported by the hub | 2.6M | 829.9k |
| Last observed | 2026-09-18 | 2026-09-18 |
An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking.
Other Reported Results
These results are listed for each model on its own, because the conditions needed to compare them are not stated or do not match. Two results that leave a condition blank are not assumed to share it.
wav2vec2-xls-r-300m-ftspeech
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| Alvenir ASR test dataset | Task automatic-speech-recognitionMetric werComparison conditions not established | 13.84 | saattrupdan Publisher reported |
Evaluated revision not stated | — |
| Danish Common Voice 8.0 | Task automatic-speech-recognitionMetric werComparison conditions not established | 17.91 | saattrupdan Publisher reported |
Evaluated revision not stated | — |
SAVRN's Notes on mms-300m-1130-forced-aligner
For aligning a transcript to its audio, this Wav2Vec2ForCTC checkpoint runs in 0.8 GB of memory at 16-bit, 0.6 GB of it weights. Mahmoud Ashraf converted the MMS-300M forced-alignment weights from torchaudio to Transformers and ships a Python package that uses much less memory than the TorchAudio alignment API. At 315M parameters over 24 layers, it uses a sliver of the cheapest setup we list, one MI300X with 192 GB at $1.85 per hour on-demand, so schedule it beside other audio work.
The license is where deployment plans change. CC BY-NC 4.0 allows sharing and adapting with credit for non-commercial purposes only, and commercial use needs separate permission from the rights holder, so a paid product cannot ship on it without that permission. Stored precision is float32, so the 9 files come to 2.5 GB on disk. Vocabulary is 31 entries; last update April 15, 2026.
SAVRN's Notes on wav2vec2-xls-r-300m-ftspeech
Transcribing the Danish parliament is what this one was tuned for. Dan Saattrup Smart fine-tuned facebook/wav2vec2-xls-r-300m on FTSpeech, 1,800 hours of transcribed speeches from the Danish parliament, into a 315M parameter CTC recognizer. The hardware question nearly answers itself: 16-bit weights are 0.6 GB and need 0.8 GB of memory, so a single MI300X at $1.85 per hour on-demand, the cheapest slot we track, is far more card than one copy needs. We would pack it beside other work on the same GPU rather than dedicate anything to it.
The license is where to slow down. It is listed as other, and the publisher says use must adhere to a license from the Danish Parliament, so read that document before any deployment. Check the publisher-reported word error rates, 13.84 on the Alvenir ASR test set and 17.91 on Danish Common Voice 8.0, against your own audio before committing.
Questions
Which is larger, mms-300m-1130-forced-aligner or wav2vec2-xls-r-300m-ftspeech?
wav2vec2-xls-r-300m-ftspeech (315M parameters) is larger than mms-300m-1130-forced-aligner (315M parameters), by the parameter counts their publishers report.
Which is cheaper to run, mms-300m-1130-forced-aligner or wav2vec2-xls-r-300m-ftspeech?
At 4-bit, mms-300m-1130-forced-aligner fits on 1x MI300X from $1.85 an hour and wav2vec2-xls-r-300m-ftspeech on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use mms-300m-1130-forced-aligner commercially?
Not without separate permission. mms-300m-1130-forced-aligner is released under Creative Commons Attribution-NonCommercial 4.0. CC BY-NC 4.0 permits sharing and adapting with credit for non-commercial purposes only. Commercial use needs separate permission from the rights holder.