add chinese, dutch. python usage. readme update

2025-07-01 18:17:27 -04:00 · 2022-12-23 00:41:12 +00:00
parent e909f2f766
commit c6fa7df3cc
3 changed files with 55 additions and 41 deletions
--- a/README.md
+++ b/README.md
@ -1,4 +1,5 @@
 <h1 align="center">WhisperX</h1>
+
 <p align="center">
  <a href="https://github.com/m-bain/whisperX/stargazers">
    <img src="https://img.shields.io/github/stars/m-bain/whisperX.svg?colorA=orange&colorB=orange&logo=github"
@ -20,15 +21,19 @@
 <p align="center">
  <a href="#what-is-it">What is it</a> •
  <a href="#setup">Setup</a> •
-  <a href="#example">Example usage</a>
+  <a href="#example">Usage</a> •
+  <a href="#other-languages">Multilingual</a> •
+  <a href="#python-usage">Python</a> •
+  <a href="#contribute">Contribute</a> •
+  <a href="EXAMPLES.md">More examples</a>
 </p>

+<h6 align="center">Made by Max Bain • :globe_with_meridians: <a href="https://www.maxbain.com">https://www.maxbain.com</a></h6>
+
 <img width="1216" align="center" alt="whisperx-arch" src="https://user-images.githubusercontent.com/36994049/208313881-903ab3ea-4932-45fd-b3dc-70876cddaaa2.png">



-<h6 align="center">Made by Max Bain • :globe_with_meridians: <a href="https://www.maxbain.com">https://www.maxbain.com</a></h6>
-
 <p align="left">Whisper-Based Automatic Speech Recognition (ASR) with improved timestamp accuracy using forced alignment.

 </p>
@ -52,7 +57,7 @@ Install this package using

 You may also need to install ffmpeg, rust etc. Follow openAI instructions here https://github.com/openai/whisper#setup.

-<h2 align="left" id="example">Example usage💬</h2>
+<h2 align="left" id="example">Usage 💬 (command line)</h2>

 ### English

@ -61,9 +66,9 @@ Run whisper on example segment (using default params)
    whisperx examples/sample01.wav


-For increased timestamp accuracy, at the cost of higher gpu mem, use a bigger alignment model e.g.
+For increased timestamp accuracy, at the cost of higher gpu mem, use bigger models e.g.

-    whisperx examples/sample01.wav --model medium.en --align_model WAV2VEC2_ASR_LARGE_LV60K_960H --output_dir examples/whisperx
+    whisperx examples/sample01.wav --model large.en --align_model WAV2VEC2_ASR_LARGE_LV60K_960H

 Result using *WhisperX* with forced alignment to wav2vec2.0 large:

@ -73,41 +78,42 @@ Compare this to original whisper out the box, where many transcriptions are out

 https://user-images.githubusercontent.com/36994049/207743923-b4f0d537-29ae-4be2-b404-bb941db73652.mov

-## Other Languages
+### Other languages

-For non-english ASR, it is best to use the `large` whisper model.
+The phoneme ASR alignment model is *language-specific*, for tested languages these models are [automatically picked from torchaudio pipelines or huggingface](https://github.com/m-bain/whisperX/blob/e909f2f766b23b2000f2d95df41f9b844ac53e49/whisperx/transcribe.py#L22).
+Just pass in the `--language` code, and use the whisper `--model large`.

-### French
-    whisperx examples/sample_fr_01.wav --model large --language fr --align_model VOXPOPULI_ASR_BASE_10K_FR --output_dir examples/whisperx
+Currently default models provided for `{en, fr, de, es, it, ja, zh, nl}`. If the detected language is not in this list, you need to find a phoneme-based ASR model from [huggingface model hub](https://huggingface.co/models) and test it on your data.


-https://user-images.githubusercontent.com/36994049/208298804-31c49d6f-6787-444e-a53f-e93c52706752.mov
-
-
-
-### German
-    whisperx examples/sample_de_01.wav --model large --language de --align_model VOXPOPULI_ASR_BASE_10K_DE --output_dir examples/whisperx
-
+#### E.g. German
+    whisperx --model large --language de examples/sample_de_01.wav

 https://user-images.githubusercontent.com/36994049/208298811-e36002ba-3698-4731-97d4-0aebd07e0eb3.mov

+## Python usage 🐍

+```python
+import whisperx

+device = "cuda" 
+audio_file = "audio.mp3"

-### Italian
-    whisperx examples/sample_it_01.wav --model large --language it --align_model VOXPOPULI_ASR_BASE_10K_IT --output_dir examples/whisperx
+# transcribe with original whisper
+model = whisperx.load_model("large", device)
+result = model.transcribe(audio_file)

+# load alignment model and metadata
+model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)

+# align whisper output
+result_aligned = whisperx.align(result["segments"], model_a, metadata, audio_file, device)

-https://user-images.githubusercontent.com/36994049/208298819-6f462b2c-8cae-4c54-b8e1-90855794efc7.mov
-
-### Japanese
-    whisperx --model large --language ja examples/sample_ja_01.wav  --align_model jonatasgrosman/wav2vec2-large-xlsr-53-japanese --output_dir examples/whisperx --align_extend 2
-
-
-
-https://user-images.githubusercontent.com/19920981/208731743-311f2360-b73b-4c60-809d-aaf3cd7e06f4.mov
+print(result["segments"]) # before alignment

+print(result_aligned["segments"]) # after alignment
+print(result_aligned["word_segments"]) # after alignment
+```

 <h2 align="left" id="limitations">Limitations ⚠️</h2>

@ -116,22 +122,30 @@ https://user-images.githubusercontent.com/19920981/208731743-311f2360-b73b-4c60-
 - Assumes the initial whisper timestamps are accurate to some degree (within margin of 2 seconds, adjust if needed -- bigger margins more prone to alignment errors)
 - Hacked this up quite quickly, there might be some errors, please raise an issue if you encounter any.

+
+<h2 align="left" id="contribute">Contribute 🧑‍🏫</h2>
+
+If you are multilingual, a major way you can contribute to this project is to find phoneme models on huggingface (or train your own) and test them on speech for the target language. If the results look good send a merge request and some examples showing its success.
+
+The next major upgrade we are working on is whisper with speaker diarization, so if you have any experience on this please share.
+
 <h2 align="left" id="coming-soon">Coming Soon 🗓</h2>

-[x] Multilingual init
+[x] ~~Multilingual init~~ done

-[x] Subtitle .ass output
+[x] ~~Subtitle .ass output~~ done

-[x] Automatic align model selection based on language detection
+[x] ~~Automatic align model selection based on language detection~~ done
+
+[x] ~~Python usage~~ done

 [ ] Incorporating word-level speaker diarization

 [ ] Inference speedup with batch processing

-<h2 align="left" id="contact">Contact</h2>
-
-Contact maxbain[at]robots[dot]ox[dot]ac[dot]uk if using this commerically.
+<h2 align="left" id="contact">Contact 📇</h2>

+Contact maxbain[at]robots[dot]ox[dot]ac[dot]uk for business things.

 <h2 align="left" id="acks">Acknowledgements 🙏</h2>

--- a/whisperx/init.py
+++ b/whisperx/init.py
@ -11,8 +11,7 @@ from tqdm import tqdm
 from .audio import load_audio, log_mel_spectrogram, pad_or_trim
 from .decoding import DecodingOptions, DecodingResult, decode, detect_language
 from .model import Whisper, ModelDimensions
-from .transcribe import transcribe
-
+from .transcribe import transcribe, load_align_model, align

 _MODELS = {
    "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@ -29,6 +29,8 @@ DEFAULT_ALIGN_MODELS_TORCH = {

 DEFAULT_ALIGN_MODELS_HF = {
    "ja": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese",
+    "zh": "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn",
+    "nl": "jonatasgrosman/wav2vec2-large-xlsr-53-dutch",
 }


@ -264,7 +266,6 @@ def transcribe(

 def align(
    transcript: Iterator[dict],
-    language: str,
    model: torch.nn.Module,
    align_model_metadata: dict,
    audio: Union[str, np.ndarray, torch.Tensor],
@ -309,7 +310,7 @@ def align(
            emissions = torch.log_softmax(emissions, dim=-1)
        emission = emissions[0].cpu().detach()
        transcription = segment['text'].strip()
-        if language not in LANGUAGES_WITHOUT_SPACES:
+        if model_lang not in LANGUAGES_WITHOUT_SPACES:
            t_words = transcription.split(' ')
        else:
            t_words = [c for c in transcription]
@ -426,7 +427,7 @@ def cli():
    parser.add_argument("--drop_non_aligned", action="store_true", help="For word .srt, whether to drop non aliged words, or merge them into neighbouring.")

    parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
-    parser.add_argument("--output_type", default="srt", choices=['all', 'srt', 'vtt', 'txt'], help="directory to save the outputs")
+    parser.add_argument("--output_type", default="srt", choices=['all', 'srt', 'vtt', 'txt'], help="File type for desired output save")

    parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")

@ -494,7 +495,7 @@ def cli():
            print(f"New language found ({result['language']})! Previous was ({align_metadata['language']}), loading new alignment model for new language...")
            align_model, align_metadata = load_align_model(result["language"], device)

-        result_aligned = align(result["segments"], result["language"], align_model, align_metadata, audio_path, device,
+        result_aligned = align(result["segments"], align_model, align_metadata, audio_path, device,
                                extend_duration=align_extend, start_from_previous=align_from_prev, drop_non_aligned_words=drop_non_aligned)
        audio_basename = os.path.basename(audio_path)

@ -518,8 +519,8 @@ def cli():
            write_srt(result_aligned["word_segments"], file=srt)

        # save ASS
-        with open(os.path.join(output_dir, audio_basename + ".ass"), "w", encoding="utf-8") as srt:
-            write_ass(result_aligned["segments"], file=srt)
+        with open(os.path.join(output_dir, audio_basename + ".ass"), "w", encoding="utf-8") as ass:
+            write_ass(result_aligned["segments"], file=ass)


 if __name__ == '__main__':