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README.md

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-# Modelo ASR Fine-tuned
+---
+license: cc-by-nc-4.0
+language:
+- en
+- de
+- es
+- fr
+library_name: nemo
+datasets:
+- librispeech_asr
+- fisher_corpus
+- Switchboard-1
+- WSJ-0
+- WSJ-1
+- National-Singapore-Corpus-Part-1
+- National-Singapore-Corpus-Part-6
+- vctk
+- voxpopuli
+- europarl
+- multilingual_librispeech
+- mozilla-foundation/common_voice_8_0
+- MLCommons/peoples_speech
+thumbnail: null
+tags:
+- automatic-speech-recognition
+- automatic-speech-translation
+- speech
+- audio
+- Transformer
+- FastConformer
+- Conformer
+- pytorch
+- NeMo
+- hf-asr-leaderboard
+widget:
+- example_title: Librispeech sample 1
+  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
+- example_title: Librispeech sample 2
+  src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
+model-index:
+- name: canary-1b
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: LibriSpeech (other)
+      type: librispeech_asr
+      config: other
+      split: test
+      args:
+        language: en
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 2.89
+  - task:
+      type: Automatic Speech Recognition
+      name: automatic-speech-recognition
+    dataset:
+      name: SPGI Speech
+      type: kensho/spgispeech
+      config: test
+      split: test
+      args:
+        language: en
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 4.79
+  - task:
+      type: Automatic Speech Recognition
+      name: automatic-speech-recognition
+    dataset:
+      name: Mozilla Common Voice 16.1
+      type: mozilla-foundation/common_voice_16_1
+      config: en
+      split: test
+      args:
+        language: en
+    metrics:
+    - name: Test WER (En)
+      type: wer
+      value: 7.97
+  - task:
+      type: Automatic Speech Recognition
+      name: automatic-speech-recognition
+    dataset:
+      name: Mozilla Common Voice 16.1
+      type: mozilla-foundation/common_voice_16_1
+      config: de
+      split: test
+      args:
+        language: de
+    metrics:
+    - name: Test WER (De)
+      type: wer
+      value: 4.61
+  - task:
+      type: Automatic Speech Recognition
+      name: automatic-speech-recognition
+    dataset:
+      name: Mozilla Common Voice 16.1
+      type: mozilla-foundation/common_voice_16_1
+      config: es
+      split: test
+      args:
+        language: es
+    metrics:
+    - name: Test WER (ES)
+      type: wer
+      value: 3.99
+  - task:
+      type: Automatic Speech Recognition
+      name: automatic-speech-recognition
+    dataset:
+      name: Mozilla Common Voice 16.1
+      type: mozilla-foundation/common_voice_16_1
+      config: fr
+      split: test
+      args:
+        language: fr
+    metrics:
+    - name: Test WER (Fr)
+      type: wer
+      value: 6.53
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: FLEURS
+      type: google/fleurs
+      config: en_us
+      split: test
+      args:
+        language: en-de
+    metrics:
+    - name: Test BLEU (En->De)
+      type: bleu
+      value: 32.15
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: FLEURS
+      type: google/fleurs
+      config: en_us
+      split: test
+      args:
+        language: en-de
+    metrics:
+    - name: Test BLEU (En->Es)
+      type: bleu
+      value: 22.66
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: FLEURS
+      type: google/fleurs
+      config: en_us
+      split: test
+      args:
+        language: en-de
+    metrics:
+    - name: Test BLEU (En->Fr)
+      type: bleu
+      value: 40.76
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: FLEURS
+      type: google/fleurs
+      config: de_de
+      split: test
+      args:
+        language: de-en
+    metrics:
+    - name: Test BLEU (De->En)
+      type: bleu
+      value: 33.98
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: FLEURS
+      type: google/fleurs
+      config: es_419
+      split: test
+      args:
+        language: es-en
+    metrics:
+    - name: Test BLEU (Es->En)
+      type: bleu
+      value: 21.80
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: FLEURS
+      type: google/fleurs
+      config: fr_fr
+      split: test
+      args:
+        language: fr-en
+    metrics:
+    - name: Test BLEU (Fr->En)
+      type: bleu
+      value: 30.95
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: COVOST
+      type: covost2
+      config: de_de
+      split: test
+      args:
+        language: de-en
+    metrics:
+    - name: Test BLEU (De->En)
+      type: bleu
+      value: 37.67
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: COVOST
+      type: covost2
+      config: es_419
+      split: test
+      args:
+        language: es-en
+    metrics:
+    - name: Test BLEU (Es->En)
+      type: bleu
+      value: 40.7
+  - task:
+      type: Automatic Speech Translation
+      name: automatic-speech-translation
+    dataset:
+      name: COVOST
+      type: covost2
+      config: fr_fr
+      split: test
+      args:
+        language: fr-en
+    metrics:
+    - name: Test BLEU (Fr->En)
+      type: bleu
+      value: 40.42
+  
+metrics:
+- wer
+- bleu
+pipeline_tag: automatic-speech-recognition
+---
 
-Este é um modelo ASR fine-tuned.
-Vocabulário: ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'ç', 'ã', 'á', 'à', 'â', 'ê', 'é', 'è', 'í', 'ì', 'î', 'õ', 'ó', 'ò', 'ô', 'ú', 'ù', 'û', ' ']
-Modelo salvo em huggingface/ASR-Char-Model-Language-pt.nemo e huggingface/ASR-Char-Model-Language-pt--val_wer=0.3373-epoch=20-last.ckpt.
+
+# Canary 1B
+
+<style>
+img {
+ display: inline;
+}
+</style>
+
+[![Model architecture](https://img.shields.io/badge/Model_Arch-FastConformer--Transformer-lightgrey#model-badge)](#model-architecture)
+| [![Model size](https://img.shields.io/badge/Params-1B-lightgrey#model-badge)](#model-architecture)
+| [![Language](https://img.shields.io/badge/Language-multilingual-lightgrey#model-badge)](#datasets)
+
+NVIDIA [NeMo Canary](https://nvidia.github.io/NeMo/blogs/2024/2024-02-canary/) is a family of multi-lingual multi-tasking models that achieves state-of-the art performance on multiple benchmarks. With 1 billion parameters, Canary-1B supports automatic speech-to-text recognition (ASR) in 4 languages (English, German, French, Spanish) and translation from English to German/French/Spanish and from German/French/Spanish to English with or without punctuation and capitalization (PnC). 
+
+## Model Architecture
+
+Canary is an encoder-decoder model with FastConformer [1] encoder and Transformer Decoder [2]. 
+With audio features extracted from the encoder, task tokens such as `<source language>`, `<target language>`, `<task>` and `<toggle PnC>` 
+are fed into the Transformer Decoder to trigger the text generation process. Canary uses a concatenated tokenizer [5] from individual 
+SentencePiece [3] tokenizers of each language, which makes it easy to scale up to more languages. 
+The Canay-1B model has 24 encoder layers and 24 layers of decoder layers in total.
+
+
+
+## NVIDIA NeMo
+
+To train, fine-tune or Transcribe with Canary, you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed Cython and latest PyTorch version.
+```
+pip install git+https://github.com/NVIDIA/[email protected]#egg=nemo_toolkit[asr]
+```
+
+
+## How to Use this Model
+
+The model is available for use in the NeMo toolkit [4], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
+
+### Loading the Model
+
+```python
+from nemo.collections.asr.models import EncDecMultiTaskModel
+
+# load model
+canary_model = EncDecMultiTaskModel.from_pretrained('nvidia/canary-1b')
+
+# update dcode params
+decode_cfg = canary_model.cfg.decoding
+decode_cfg.beam.beam_size = 1
+canary_model.change_decoding_strategy(decode_cfg)
+```
+
+### Input Format
+Input to Canary can be either a list of paths to audio files or a jsonl manifest file.
+
+If the input is a list of paths, Canary assumes that the audio is English and Transcribes it. I.e., Canary default behaviour is English ASR. 
+```python
+predicted_text = canary_model.transcribe(
+    paths2audio_files=['path1.wav', 'path2.wav'],
+    batch_size=16,  # batch size to run the inference with
+)[0].text
+```
+
+To use Canary for transcribing other supported languages or perform Speech-to-Text translation, specify the input as jsonl manifest file, where each line in the file is a dictionary containing the following fields: 
+
+```yaml
+# Example of a line in input_manifest.json
+{
+    "audio_filepath": "/path/to/audio.wav",  # path to the audio file
+    "duration": 1000,  # duration of the audio, can be set to `None` if using NeMo main branch
+    "taskname": "asr",  # use "s2t_translation" for speech-to-text translation with r1.23, or "ast" if using the NeMo main branch
+    "source_lang": "en",  # language of the audio input, set `source_lang`==`target_lang` for ASR, choices=['en','de','es','fr']
+    "target_lang": "en",  # language of the text output, choices=['en','de','es','fr']
+    "pnc": "yes",  # whether to have PnC output, choices=['yes', 'no']
+    "answer": "na", 
+}
+```
+
+and then use:
+```python
+predicted_text = canary_model.transcribe(
+    "<path to input manifest file>",
+    batch_size=16,  # batch size to run the inference with
+)[0].text
+```
+
+
+### Automatic Speech-to-text Recognition (ASR)
+
+An example manifest for transcribing English audios can be:
+
+```yaml
+# Example of a line in input_manifest.json
+{
+    "audio_filepath": "/path/to/audio.wav",  # path to the audio file
+    "duration": 1000,  # duration of the audio, can be set to `None` if using NeMo main branch
+    "taskname": "asr",  
+    "source_lang": "en", # language of the audio input, set `source_lang`==`target_lang` for ASR, choices=['en','de','es','fr']
+    "target_lang": "en", # language of the text output, choices=['en','de','es','fr']
+    "pnc": "yes",  # whether to have PnC output, choices=['yes', 'no']
+    "answer": "na", 
+}
+```
+
+
+### Automatic Speech-to-text Translation (AST)
+
+An example manifest for transcribing English audios into German text can be:
+
+```yaml
+# Example of a line in input_manifest.json
+{
+    "audio_filepath": "/path/to/audio.wav",  # path to the audio file
+    "duration": 1000,  # duration of the audio, can be set to `None` if using NeMo main branch
+    "taskname": "s2t_translation", # r1.23 only recognizes "s2t_translation", but "ast" is supported if using the NeMo main branch
+    "source_lang": "en", # language of the audio input, choices=['en','de','es','fr']
+    "target_lang": "de", # language of the text output, choices=['en','de','es','fr']
+    "pnc": "yes",  # whether to have PnC output, choices=['yes', 'no']
+    "answer": "na" 
+}
+```
+
+Alternatively, one can use `transcribe_speech.py` script to do the same. 
+
+```bash
+python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
+ pretrained_name="nvidia/canary-1b" 
+ audio_dir="<path to audio_directory>" # transcribes all the wav files in audio_directory
+```
+
+
+```bash
+python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
+ pretrained_name="nvidia/canary-1b" 
+ dataset_manifest="<path to manifest file>" 
+```
+
+
+### Input
+
+This model accepts single channel (mono) audio sampled at 16000 Hz, along with the task/languages/PnC tags as input.
+
+### Output
+
+The model outputs the transcribed/translated text corresponding to the input audio, in the specified target language and with or without punctuation and capitalization.
+
+
+
+## Training
+
+Canary-1B is trained using the  NVIDIA NeMo toolkit [4] for 150k steps with dynamic bucketing and a batch duration of 360s per GPU on 128 NVIDIA A100 80GB GPUs. 
+The model can be trained using this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_multitask/speech_to_text_aed.py) and [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/speech_multitask/fast-conformer_aed.yaml).
+
+The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
+
+
+### Datasets
+
+The Canary-1B model is trained on a total of 85k hrs of speech data. It consists of 31k hrs of public data, 20k hrs collected by [Suno](https://suno.ai/), and 34k hrs of in-house data. 
+
+The constituents of public data are as follows. 
+
+#### English (25.5k hours)
+- Librispeech 960 hours
+- Fisher Corpus
+- Switchboard-1 Dataset
+- WSJ-0 and WSJ-1
+- National Speech Corpus (Part 1, Part 6)
+- VCTK
+- VoxPopuli (EN)
+- Europarl-ASR (EN)
+- Multilingual Librispeech (MLS EN) - 2,000 hour subset
+- Mozilla Common Voice (v7.0)
+- People's Speech - 12,000 hour subset
+- Mozilla Common Voice (v11.0)  - 1,474 hour subset
+
+#### German (2.5k hours)
+- Mozilla Common Voice (v12.0)  - 800 hour subset
+- Multilingual Librispeech (MLS DE) - 1,500 hour subset
+- VoxPopuli (DE) - 200 hr subset
+
+#### Spanish (1.4k hours)
+- Mozilla Common Voice (v12.0)  - 395 hour subset
+- Multilingual Librispeech (MLS ES) - 780 hour subset
+- VoxPopuli (ES) - 108 hour subset
+- Fisher  - 141 hour subset
+
+#### French (1.8k hours)
+- Mozilla Common Voice (v12.0)  - 708 hour subset
+- Multilingual Librispeech (MLS FR) - 926 hour subset
+- VoxPopuli (FR) - 165 hour subset
+
+
+## Performance
+
+In both ASR and AST experiments, predictions were generated using beam search with width 5 and length penalty 1.0.
+
+### ASR Performance (w/o PnC) 
+
+The ASR performance is measured with word error rate (WER), and we process the groundtruth and predicted text with [whisper-normalizer](https://pypi.org/project/whisper-normalizer/).
+
+WER on [MCV-16.1](https://commonvoice.mozilla.org/en/datasets) test set:
+
+| **Version** | **Model**     | **En**   | **De**   | **Es**   | **Fr**   |
+|:---------:|:-----------:|:------:|:------:|:------:|:------:|
+| 1.23.0  | canary-1b | 7.97 | 4.61 | 3.99 | 6.53 |
+
+
+WER on [MLS](https://huggingface.co/datasets/facebook/multilingual_librispeech) test set:
+
+| **Version** | **Model**     | **En**   | **De**   | **Es**   | **Fr**   |
+|:---------:|:-----------:|:------:|:------:|:------:|:------:|
+| 1.23.0  | canary-1b | 3.06 | 4.19 | 3.15 | 4.12 |
+
+
+More details on evaluation can be found at [HuggingFace ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)
+
+### AST Performance
+
+We evaluate AST performance with [BLEU score](https://lightning.ai/docs/torchmetrics/stable/text/sacre_bleu_score.html), and use native annotations with punctuation and capitalization in the datasets.
+
+BLEU score on [FLEURS](https://huggingface.co/datasets/google/fleurs) test set:
+
+| **Version** | **Model** | **En->De** | **En->Es** | **En->Fr** | **De->En** | **Es->En** | **Fr->En** |
+|:-----------:|:---------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|
+| 1.23.0      | canary-1b | 32.15	   | 22.66      | 40.76      | 33.98      | 21.80      | 30.95      |
+
+
+BLEU score on [COVOST-v2](https://github.com/facebookresearch/covost) test set:
+
+| **Version** | **Model** | **De->En** | **Es->En** | **Fr->En** |
+|:-----------:|:---------:|:----------:|:----------:|:----------:|
+| 1.23.0      | canary-1b | 37.67      | 40.7       | 40.42      |
+
+BLEU score on [mExpresso](https://huggingface.co/facebook/seamless-expressive#mexpresso-multilingual-expresso) test set:
+
+| **Version** | **Model** | **En->De** | **En->Es** | **En->Fr** |
+|:-----------:|:---------:|:----------:|:----------:|:----------:|
+| 1.23.0      | canary-1b | 23.84      |   35.74    | 28.29      |
+
+## Model Fairness Evaluation
+
+As outlined in the paper "Towards Measuring Fairness in AI: the Casual Conversations Dataset", we assessed the Canary-1B model for fairness. The model was evaluated on the CausalConversations-v1 dataset, and the results are reported as follows:
+
+### Gender Bias:
+
+| Gender | Male | Female | N/A | Other |
+| :--- | :--- | :--- | :--- | :--- |
+| Num utterances | 19325 | 24532 | 926 | 33 |
+| % WER | 14.64 | 12.92 | 17.88 | 126.92 |
+
+### Age Bias:
+
+| Age Group | (18-30) | (31-45) | (46-85) | (1-100) |
+| :--- | :--- | :--- | :--- | :--- |
+| Num utterances | 15956 | 14585 | 13349 | 43890 |
+| % WER | 14.64 | 13.07 | 13.47 | 13.76 |
+
+(Error rates for fairness evaluation are determined by normalizing both the reference and predicted text, similar to the methods used in the evaluations found at https://github.com/huggingface/open_asr_leaderboard.)
+
+## NVIDIA Riva: Deployment
+
+[NVIDIA Riva](https://developer.nvidia.com/riva), is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded. 
+Additionally, Riva provides: 
+
+* World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours 
+* Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization 
+* Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support 
+
+Canary is available as a NIM endpoint via Riva. Try the model yourself here: [https://build.nvidia.com/nvidia/canary-1b-asr](https://build.nvidia.com/nvidia/canary-1b-asr). 
+
+
+## References
+[1] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)
+
+[2] [Attention is all you need](https://arxiv.org/abs/1706.03762)
+
+[3] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)
+
+[4] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
+
+[5] [Unified Model for Code-Switching Speech Recognition and Language Identification Based on Concatenated Tokenizer](https://aclanthology.org/2023.calcs-1.7.pdf)
+
+## Licence
+
+License to use this model is covered by the [CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/deed.en#:~:text=NonCommercial%20%E2%80%94%20You%20may%20not%20use,doing%20anything%20the%20license%20permits.). By downloading the public and release version of the model, you accept the terms and conditions of the CC-BY-NC-4.0 license.