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Merge japanese-to-english multilingual branch #1860
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```shell | ||
./zipformer/streaming_decode.py \ | ||
--epoch 28 \ |
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Since the Reazonspeech is a large dataset, I suggest that you replace --epoch
with --iter
. See also RESULTS.md
from our Gigaspeech recipe. You can find example usages of --iter
there.
With regards to Setup Python 3.10.15 issue - how do I resolve this issue? I didn't change anything related to Python issue. |
please change icefall/.github/workflows/style_check.yml Line 39 in ab91112
to
That is, change It is an issue of GitHub actions and is not related to your PR. |
Looks like this hasn't resolved the issue. |
Please use
not
|
@JinZr Can you have a review? |
ok! i’ll look into it this weekend
Best Regards
Jin
…On Mon, 20 Jan 2025 at 18:20 Fangjun Kuang ***@***.***> wrote:
@JinZr <https://github.com/JinZr> Can you have a review?
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i’ve completed reviewing this PR, and it looks great overall!
there are also some unnecessary changes to other dependencies that might need to be addressed before merging
icefall/utils.py
Outdated
@@ -644,7 +644,8 @@ def write_error_stats( | |||
results[i] = (cut_id, ref, hyp) | |||
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for cut_id, ref, hyp in results: | |||
ali = kaldialign.align(ref, hyp, ERR, sclite_mode=sclite_mode) | |||
# ali = kaldialign.align(ref, hyp, ERR, sclite_mode=sclite_mode) | |||
ali = kaldialign.align(ref, hyp, ERR) |
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Could you provide some context on why the ''sclite_mode'' argument was removed?
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please avoid unnecessary modifications to built-in files
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please avoid unnecessary modifications to built-in files, thanks
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thanks!
i left a few commits to remove changed applied to built-in scripts, i think the pr is ready to be merged now.
Great, thank you! |
to clarify, there's no further action needed on my end, right? |
sure! pls feel free to merge it
Best Regards
Jin
…On Mon, 3 Feb 2025 at 01:28 Machiko Bailey ***@***.***> wrote:
thanks!
i left a few commits to remove changed applied to built-in scripts, i
think the pr is ready to be merged now.
Great, thank you!
to clarify, there's no further action needed on my end, right?
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thanks, i don't have write access to merge so i will kindly wait :) thanks for the review |
sorry i didnt realize that, i'll do the operation now |
I am investigating improving English accuracy of the recently merged multi_ja_en model and saw that both Librispeech and mult_zh_en models use 3x speed perturbation. Is there somewhere I can access that version of Librispeech data? Also, did multi_zh_en ever look into using gigaspeech model for English side? |
hi, regarding the librispeech data, you can easily conduct the 3-time
speech perturbation by toggling the ``--perturb-speed`` in the
``compute_librispeech_fbank.py`` to True, the script is under
``egs/librispeech/ASR/local/``.
i didn't conduct any experiment on gigaspeech corpus while making the
``multi_zh_en`` recipe, but have those part of data involved in model
training should be helpful
…On Thu, Feb 27, 2025 at 11:13 AM Machiko Bailey ***@***.***> wrote:
I am investigating improving English accuracy of the recently merged
multi_ja_en model and saw that both Librispeech and mult_zh_en models use
3x speed perturbation. Is there somewhere I can access that version of
Librispeech data? Also, did multi_zh_en ever look into using gigaspeech
model for English side?
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[image: baileyeet]*baileyeet* left a comment (k2-fsa/icefall#1860)
<#1860 (comment)>
I am investigating improving English accuracy of the recently merged
multi_ja_en model and saw that both Librispeech and mult_zh_en models use
3x speed perturbation. Is there somewhere I can access that version of
Librispeech data? Also, did multi_zh_en ever look into using gigaspeech
model for English side?
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Hello, I'm working together with @baileyeet on improving the English accuracy of this model. We are considering training a version of this model using a larger English dataset. One dataset we are considering is the People's Speech corpus. I see there was a pull request by @yfyeung that added support for data loading of this dataset in the past, but it seems it was removed because of poor training results. Here are the relevant PRs: #1101 #1778 . Can anyone vouch for the quality of the People's Speech corpus, or explain why it might not be a suitable dataset? It seems that it has not yet been evaluated with results. As an alternative, we are considering using GigaSpeech, but it has less data, and it seemingly has more restrictive licensing. Would this be a better choice? Are there any other English labeled datasets that would be better, apart from these two? Thank you for any responses! |
Hi, according to the paper on the People's Speech corpus, models trained on it report the following WER (Word Error Rate) on LibriSpeech: dev-clean: 9.93% |
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