End of training

README.md

@@ -1,569 +1,91 @@
 ---
 language:
 - en
-tags:
-- audio
-- automatic-speech-recognition
-- transformers.js
-inference: false
-widget:
-  - src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
-    example_title: Librispeech sample 1
-    output:
-      text: going along slushy country roads and speaking to damp audiences in draughty schoolrooms day after day for a fortnight he'll have to put in an appearance at some place of worship on sunday morning and he can come to us immediately afterwards
-  - src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
-    example_title: Librispeech sample 2
-    output:
-      text: before he had time to answer a much-encumbered vera burst into the room with the question i say can i leave these here these were a small black pig and a lusty specimen of black-red game-cock
-pipeline_tag: automatic-speech-recognition
 license: mit
-library_name: transformers
+base_model: distil-whisper/distil-small.en
+tags:
+- generated_from_trainer
+datasets:
+- atc
+metrics:
+- wer
+model-index:
+- name: Whisper Large v3 1500 Epochs 2 - nullonesix
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: atc
+      type: atc
+      args: 'config: en, split: test'
+    metrics:
+    - name: Wer
+      type: wer
+      value: 39.23487544483986
 ---
 
-# Distil-Whisper: distil-small.en
-
-Distil-Whisper was proposed in the paper [Robust Knowledge Distillation via Large-Scale Pseudo Labelling](https://arxiv.org/abs/2311.00430).
-It is a distilled version of the Whisper model that is **6 times faster**, 49% smaller, and performs **within 1% WER** 
-on out-of-distribution evaluation sets. 
-
-This is the repository for distil-small.en, a distilled variant of [Whisper small.en](https://huggingface.co/openai/whisper-small.en).
-It is the **smallest Distil-Whisper checkpoint**, with just 166M parameters, making it the ideal choice for memory
-constrained applications (e.g. on-device).
-
-For most other applications, the [distil-medium.en](https://huggingface.co/distil-whisper/distil-medium.en)
-or [distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2) checkpoints are recommended, since they are 
-both faster and achieve better WER results:
-
-| Model                                                                      | Params / M | Rel. Latency ↑ | Short-Form WER ↓ | Long-Form WER ↓ |
-|----------------------------------------------------------------------------|------------|----------------|------------------|-----------------|
-| [large-v3](https://huggingface.co/openai/whisper-large-v3)                 | 1550       | 1.0            | **8.4**          | 11.0            |
-| [large-v2](https://huggingface.co/openai/whisper-large-v2)                 | 1550       | 1.0            | 9.1              | 11.7            |
-|                                                                            |            |                |                  |                 |
-| [distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3)   | 756        | 6.3            | 9.7              | **10.8**        |
-| [distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2)   | 756        | 5.8            | 10.1             | 11.6            |
-| [distil-medium.en](https://huggingface.co/distil-whisper/distil-medium.en) | 394        | **6.8**        | 11.1             | 12.4            |
-| [distil-small.en](https://huggingface.co/distil-whisper/distil-small.en)   | **166**    | 5.6            | 12.1             | 12.8            |
-
-**Note:** Distil-Whisper is currently only available for English speech recognition. We are working with the community 
-to distill Whisper on other languages. If you are interested in distilling Whisper in your language, check out the 
-provided [training code](https://github.com/huggingface/distil-whisper/tree/main/training). We will update the 
-[Distil-Whisper repository](https://github.com/huggingface/distil-whisper/) with multilingual checkpoints when ready!
-
-### Why is distil-small.en slower than distil-large-v2?
-
-While [distil-medium.en](https://huggingface.co/distil-whisper/distil-medium.en) and [distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2)
-use two decoder layers each, distil-small.en uses four. Using more decoder layers improves the WER performance of the 
-model, at the expense of slower inference speed. We found that four layers was the minimum required to get reasonable 
-WER performance for `distil-small.en`, where it performs to within 3% WER of Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2)
-while being 5.6x faster. When we tried distilling with just two layers, the model was over 5% worse than large-v2, albeit
-7.8x faster. We leave distilling a two layer small.en model as future works.
-
-## Usage
-
-Distil-Whisper is supported in Hugging Face 🤗 Transformers from version 4.35 onwards. To run the model, first 
-install the latest version of the Transformers library. For this example, we'll also install 🤗 Datasets to load toy 
-audio dataset from the Hugging Face Hub:
-
-```bash
-pip install --upgrade pip
-pip install --upgrade transformers accelerate datasets[audio]
-```
-
-### Short-Form Transcription
-
-The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
-class to transcribe short-form audio files (< 30-seconds) as follows:
-
-```python
-import torch
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
-from datasets import load_dataset
-
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-
-model_id = "distil-whisper/distil-small.en"
-
-model = AutoModelForSpeechSeq2Seq.from_pretrained(
-    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
-)
-model.to(device)
-
-processor = AutoProcessor.from_pretrained(model_id)
-
-pipe = pipeline(
-    "automatic-speech-recognition",
-    model=model,
-    tokenizer=processor.tokenizer,
-    feature_extractor=processor.feature_extractor,
-    max_new_tokens=128,
-    torch_dtype=torch_dtype,
-    device=device,
-)
-
-dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
-sample = dataset[0]["audio"]
-
-result = pipe(sample)
-print(result["text"])
-```
-
-To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline:
-```diff
-- result = pipe(sample)
-+ result = pipe("audio.mp3")
-```
-
-### Long-Form Transcription
-
-Distil-Whisper uses a chunked algorithm to transcribe long-form audio files (> 30-seconds). In practice, this chunked long-form algorithm 
-is 9x faster than the sequential algorithm proposed by OpenAI in the Whisper paper (see Table 7 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430)).
-
-To enable chunking, pass the `chunk_length_s` parameter to the `pipeline`. For Distil-Whisper, a chunk length of 15-seconds
-is optimal. To activate batching, pass the argument `batch_size`:
-
-```python
-import torch
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
-from datasets import load_dataset
-
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-
-model_id = "distil-whisper/distil-small.en"
-
-model = AutoModelForSpeechSeq2Seq.from_pretrained(
-    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
-)
-model.to(device)
-
-processor = AutoProcessor.from_pretrained(model_id)
-
-pipe = pipeline(
-    "automatic-speech-recognition",
-    model=model,
-    tokenizer=processor.tokenizer,
-    feature_extractor=processor.feature_extractor,
-    max_new_tokens=128,
-    chunk_length_s=15,
-    batch_size=16,
-    torch_dtype=torch_dtype,
-    device=device,
-)
-
-dataset = load_dataset("distil-whisper/librispeech_long", "default", split="validation")
-sample = dataset[0]["audio"]
-
-result = pipe(sample)
-print(result["text"])
-```
-
-<!---
-**Tip:** The pipeline can also be used to transcribe an audio file from a remote URL, for example:
-
-```python
-result = pipe("https://huggingface.co/datasets/sanchit-gandhi/librispeech_long/resolve/main/audio.wav")
-```
---->
-
-### Speculative Decoding
-
-Distil-Whisper can be used as an assistant model to Whisper for [speculative decoding](https://huggingface.co/blog/whisper-speculative-decoding). 
-Speculative decoding mathematically ensures the exact same outputs as Whisper are obtained while being 2 times faster. 
-This makes it the perfect drop-in replacement for existing Whisper pipelines, since the same outputs are guaranteed.
-
-In the following code-snippet, we load the assistant Distil-Whisper model standalone to the main Whisper pipeline. We then
-specify it as the "assistant model" for generation:
-
-```python
-from transformers import pipeline, AutoModelForSpeechSeq2Seq, AutoProcessor
-import torch
-from datasets import load_dataset
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-
-assistant_model_id = "distil-whisper/distil-small.en"
-
-assistant_model = AutoModelForSpeechSeq2Seq.from_pretrained(
-    assistant_model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
-)
-assistant_model.to(device)
-
-model_id = "openai/whisper-medium.en"
-
-model = AutoModelForSpeechSeq2Seq.from_pretrained(
-    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
-)
-model.to(device)
-
-processor = AutoProcessor.from_pretrained(model_id)
-
-pipe = pipeline(
-    "automatic-speech-recognition",
-    model=model,
-    tokenizer=processor.tokenizer,
-    feature_extractor=processor.feature_extractor,
-    max_new_tokens=128,
-    generate_kwargs={"assistant_model": assistant_model},
-    torch_dtype=torch_dtype,
-    device=device,
-)
-
-dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
-sample = dataset[0]["audio"]
-
-result = pipe(sample)
-print(result["text"])
-```
-
-## Additional Speed & Memory Improvements
-
-You can apply additional speed and memory improvements to Distil-Whisper which we cover in the following.
-
-### Flash Attention
-
-We recommend using [Flash-Attention 2](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#flashattention-2) if your GPU allows for it.
-To do so, you first need to install [Flash Attention](https://github.com/Dao-AILab/flash-attention):
-
-```
-pip install flash-attn --no-build-isolation
-```
-
-and then all you have to do is to pass `use_flash_attention_2=True` to `from_pretrained`:
-
-```diff
-- model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True)
-+ model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True, use_flash_attention_2=True)
-```
-
-### Torch Scale-Product-Attention (SDPA)
-
-If your GPU does not support Flash Attention, we recommend making use of [BetterTransformers](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#bettertransformer).
-To do so, you first need to install optimum:
-
-```
-pip install --upgrade optimum
-```
-
-And then convert your model to a "BetterTransformer" model before using it:
-
-```diff
-model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True)
-+ model = model.to_bettertransformer()
-```
-
-### Running Distil-Whisper in `openai-whisper`
-
-To use the model in the original Whisper format, first ensure you have the [`openai-whisper`](https://pypi.org/project/openai-whisper/) package installed:
-
-```bash
-pip install --upgrade openai-whisper
-```
-
-The following code-snippet demonstrates how to transcribe a sample file from the LibriSpeech dataset loaded using 
-🤗 Datasets:
-
-```python
-import torch
-from datasets import load_dataset
-from huggingface_hub import hf_hub_download
-from whisper import load_model, transcribe
-
-distil_small_en = hf_hub_download(repo_id="distil-whisper/distil-small.en", filename="original-model.bin")
-model = load_model(distil_small_en)
-
-dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
-sample = dataset[0]["audio"]["array"]
-sample = torch.from_numpy(sample).float()
-
-pred_out = transcribe(model, audio=sample)
-print(pred_out["text"])
-```
-
-Note that the model weights will be downloaded and saved to your cache the first time you run the example. Subsequently,
-you can re-use the same example, and the weights will be loaded directly from your cache without having to download them
-again.
-
-To transcribe a local audio file, simply pass the path to the audio file as the `audio` argument to transcribe:
-
-```python
-pred_out = transcribe(model, audio="audio.mp3")
-```
-
-### Whisper.cpp
-
-Distil-Whisper can be run from the [Whisper.cpp](https://github.com/ggerganov/whisper.cpp) repository with the original 
-sequential long-form transcription algorithm. In a [provisional benchmark](https://github.com/ggerganov/whisper.cpp/pull/1424#issuecomment-1793513399) 
-on Mac M1, `distil-small.en` is over 4x faster than `large-v2`, while performing to within 1.4% WER over long-form audio.
-
-Steps for getting started:
-1. Clone the Whisper.cpp repository:
-```
-git clone https://github.com/ggerganov/whisper.cpp.git
-cd whisper.cpp
-```
-2. Download the ggml weights for `distil-small.en` from the Hugging Face Hub:
-
-```bash
-python -c "from huggingface_hub import hf_hub_download; hf_hub_download(repo_id='distil-whisper/distil-small.en', filename='ggml-distil-small.en.bin', local_dir='./models')"
-```
-
-Note that if you do not have the `huggingface_hub` package installed, you can also download the weights with `wget`:
-
-```bash
-wget https://huggingface.co/distil-whisper/distil-small.en/resolve/main/ggml-distil-small.en.bin -P ./models
-```
-
-3. Run inference using the provided sample audio:
-
-```bash
-make -j && ./main -m models/ggml-distil-small.en.bin -f samples/jfk.wav
-```
-
-### Transformers.js
-
-Distil-Whisper can even run completely in your web browser with [Transformers.js](http://github.com/xenova/transformers.js):
-
-1. Install Transformers.js from [NPM](https://www.npmjs.com/package/@xenova/transformers):
-```bash
-npm i @xenova/transformers
-```
-
-2. Import the library and perform inference with the pipeline API.
-```js
-import { pipeline } from '@xenova/transformers';
-
-const transcriber = await pipeline('automatic-speech-recognition', 'distil-whisper/distil-small.en');
-
-const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
-const output = await transcriber(url);
-// { text: " And so my fellow Americans, ask not what your country can do for you. Ask what you can do for your country." }
-```
-
-Check out the online [Distil-Whisper Web demo](https://huggingface.co/spaces/Xenova/distil-whisper-web) to try it out yourself. As you'll see, it runs locally in your browser: no server required!
-
-See the [docs](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.AutomaticSpeechRecognitionPipeline) for more information.
-
-### Candle
-
-Coming soon!
-
-<!---
-
-Through an integration with Hugging Face [Candle](https://github.com/huggingface/candle/tree/main) 🕯️, Distil-Whisper is 
-now available in the Rust library 🦀
-
-Benefit from:
-* Optimised CPU backend with optional MKL support for x86 and Accelerate for Macs 
-* CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL
-* WASM support: run Distil-Whisper in a browser
-
-Steps for getting started:
-1. Install [`candle-core`](https://github.com/huggingface/candle/tree/main/candle-core) as explained [here](https://huggingface.github.io/candle/guide/installation.html)
-2. Clone the `candle` repository locally:
-```
-git clone https://github.com/huggingface/candle.git
-```
-3. Enter the example directory for [Whisper](https://github.com/huggingface/candle/tree/main/candle-examples/examples/whisper):
-```
-cd candle/candle-examples/examples/whisper
-```
-4. Run an example:
-```
-cargo run --example whisper --release -- --model distil-small.en
-```
-5. To specify your own audio file, add the `--input` flag:
-```
-cargo run --example whisper --release -- --model distil-small.en --input audio.wav
-```
-
---->
-
-### 8bit & 4bit Quantization
-
-Coming soon!
-
-## Model Details
-
-Distil-Whisper inherits the encoder-decoder architecture from Whisper. The encoder maps a sequence of speech vector 
-inputs to a sequence of hidden-state vectors. The decoder auto-regressively predicts text tokens, conditional on all 
-previous tokens and the encoder hidden-states. Consequently, the encoder is only run forward once, whereas the decoder 
-is run as many times as the number of tokens generated. In practice, this means the decoder accounts for over 90% of 
-total inference time. Thus, to optimise for latency, the focus is on minimising the inference time of the decoder.
-
-To distill the Whisper model, we reduce the number of decoder layers while keeping the encoder fixed. 
-The encoder (shown in green) is entirely copied from the teacher to the student and frozen during training. 
-The student's decoder consists of a subset of the teacher decoder layers, which are intialised from maximally spaced layers.
-The model is then trained on a weighted sum of the KL divergence and pseudo-label loss terms.
-
-<p align="center">
-  <img src="https://huggingface.co/datasets/distil-whisper/figures/resolve/main/architecture.png?raw=true" width="600"/>
-</p>
-
-## Evaluation
-
-The following code-snippets demonstrates how to evaluate the Distil-Whisper model on the LibriSpeech validation.clean 
-dataset with [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet), meaning no 
-audio data has to be downloaded to your local device.
-
-First, we need to install the required packages, including 🤗 Datasets to stream and load the audio data, and 🤗 Evaluate to 
-perform the WER calculation:
-
-```bash
-pip install --upgrade pip
-pip install --upgrade transformers datasets[audio] evaluate jiwer
-```
-
-Evaluation can then be run end-to-end with the following example: 
-
-```python
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
-from transformers.models.whisper.english_normalizer import EnglishTextNormalizer
-from datasets import load_dataset
-from evaluate import load
-import torch
-from tqdm import tqdm
-
-# define our torch configuration
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-
-model_id = "distil-whisper/distil-small.en"
-
-# load the model + processor
-model =  AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, use_safetensors=True, low_cpu_mem_usage=True)
-model = model.to(device)
-processor = AutoProcessor.from_pretrained(model_id)
-
-# load the dataset with streaming mode
-dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
-
-# define the evaluation metric
-wer_metric = load("wer")
-normalizer = EnglishTextNormalizer(processor.tokenizer.english_spelling_normalizer)
-
-def inference(batch):
-    # 1. Pre-process the audio data to log-mel spectrogram inputs
-    audio = [sample["array"] for sample in batch["audio"]]
-    input_features = processor(audio, sampling_rate=batch["audio"][0]["sampling_rate"], return_tensors="pt").input_features
-    input_features = input_features.to(device, dtype=torch_dtype)
-    
-    # 2. Auto-regressively generate the predicted token ids
-    pred_ids = model.generate(input_features, max_new_tokens=128)
-    
-    # 3. Decode the token ids to the final transcription
-    batch["transcription"] = processor.batch_decode(pred_ids, skip_special_tokens=True)
-    batch["reference"] = batch["text"]
-    return batch
-
-dataset = dataset.map(function=inference, batched=True, batch_size=16)
-
-all_transcriptions = []
-all_references = []
-
-# iterate over the dataset and run inference
-for i, result in tqdm(enumerate(dataset), desc="Evaluating..."):
-    all_transcriptions.append(result["transcription"])
-    all_references.append(result["reference"])
-
-# normalize predictions and references
-all_transcriptions = [normalizer(transcription) for transcription in all_transcriptions]
-all_references = [normalizer(reference) for reference in all_references]
-
-# compute the WER metric
-wer = 100 * wer_metric.compute(predictions=all_transcriptions, references=all_references)
-print(wer)
-
-```
-**Print Output:**
-```
-3.4326070294536297
-```
-
-## Intended Use
-
-Distil-Whisper is intended to be a drop-in replacement for Whisper on English speech recognition. In particular, it 
-achieves comparable WER results over out-of-distribution test data, while being 6x faster over both short and long-form 
-audio.
-
-## Data
-
-Distil-Whisper is trained on 22,000 hours of audio data from 9 open-source, permissively licensed speech datasets on the 
-Hugging Face Hub:
-
-| Dataset                                                                                 | Size / h | Speakers | Domain                      | Licence         |
-|-----------------------------------------------------------------------------------------|----------|----------|-----------------------------|-----------------|
-| [People's Speech](https://huggingface.co/datasets/MLCommons/peoples_speech)             | 12,000   | unknown  | Internet Archive            | CC-BY-SA-4.0    |
-| [Common Voice 13](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0) | 3,000    | unknown  | Narrated Wikipedia          | CC0-1.0         |
-| [GigaSpeech](https://huggingface.co/datasets/speechcolab/gigaspeech)                    | 2,500    | unknown  | Audiobook, podcast, YouTube | apache-2.0      |
-| Fisher                                                                                  | 1,960    | 11,900   | Telephone conversations     | LDC             |
-| [LibriSpeech](https://huggingface.co/datasets/librispeech_asr)                          | 960      | 2,480    | Audiobooks                  | CC-BY-4.0       |
-| [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli)                         | 540      | 1,310    | European Parliament         | CC0             |
-| [TED-LIUM](https://huggingface.co/datasets/LIUM/tedlium)                                | 450      | 2,030    | TED talks                   | CC-BY-NC-ND 3.0 |
-| SwitchBoard                                                                             | 260      | 540      | Telephone conversations     | LDC             |
-| [AMI](https://huggingface.co/datasets/edinburghcstr/ami)                                | 100      | unknown  | Meetings                    | CC-BY-4.0       |
-||||||
-| **Total**                                                                               | 21,770   | 18,260+  |                             |                 |
-
-The combined dataset spans 10 distinct domains and over 50k speakers. The diversity of this dataset is crucial to ensuring 
-the distilled model is robust to audio distributions and noise. 
-
-The audio data is then pseudo-labelled using the Whisper large-v2 model: we use Whisper to generate predictions for all 
-the audio in our training set and use these as the target labels during training. Using pseudo-labels ensures that the 
-transcriptions are consistently formatted across datasets and provides sequence-level distillation signal during training.
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-## WER Filter
+# Whisper Large v3 1500 Epochs 2 - nullonesix
 
-The Whisper pseudo-label predictions are subject to mis-transcriptions and hallucinations. To ensure we only train on 
-accurate pseudo-labels, we employ a simple WER heuristic during training. First, we normalise the Whisper pseudo-labels
-and the ground truth labels provided by each dataset. We then compute the WER between these labels. If the WER exceeds
-a specified threshold, we discard the training example. Otherwise, we keep it for training.
+This model is a fine-tuned version of [distil-whisper/distil-small.en](https://huggingface.co/distil-whisper/distil-small.en) on the atc dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.4151
+- Wer: 39.2349
 
-Section 9.2 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430) demonstrates the effectiveness of this filter for improving downstream performance
-of the distilled model. We also partially attribute Distil-Whisper's robustness to hallucinations to this filter.
+## Model description
 
-## Training
+More information needed
 
-The model was trained for 50,000 optimisation steps (or 12 epochs) with batch size 2056. The Tensorboard training logs can 
-be found under: https://huggingface.co/distil-whisper/distil-small.en/tensorboard?params=scalars#frame
+## Intended uses & limitations
 
-## Results
+More information needed
 
-The distilled model performs to within 1% WER of Whisper on out-of-distribution (OOD) short-form audio, and outperforms Whisper
-by 0.1% on OOD long-form audio. This performance gain is attributed to lower hallucinations.
+## Training and evaluation data
 
-For a detailed per-dataset breakdown of the evaluation results, refer to Tables 16 and 17 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430)
+More information needed
 
-Distil-Whisper is also evaluated on the [ESB benchmark](https://arxiv.org/abs/2210.13352) datasets as part of the [OpenASR leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard),
-where it performs to within 0.2% WER of Whisper.
+## Training procedure
 
-## Reproducing Distil-Whisper
+### Training hyperparameters
 
-Training and evaluation code to reproduce Distil-Whisper is available under the Distil-Whisper repository: https://github.com/huggingface/distil-whisper/tree/main/training
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- training_steps: 1500
+- mixed_precision_training: Native AMP
 
-## License
+### Training results
 
-Distil-Whisper inherits the [MIT license](https://github.com/huggingface/distil-whisper/blob/main/LICENSE) from OpenAI's Whisper model.
+| Training Loss | Epoch   | Step | Validation Loss | Wer     |
+|:-------------:|:-------:|:----:|:---------------:|:-------:|
+| 2.8313        | 3.5714  | 100  | 2.7177          | 74.1548 |
+| 1.1366        | 7.1429  | 200  | 1.6407          | 63.0338 |
+| 0.4394        | 10.7143 | 300  | 1.4737          | 47.4644 |
+| 0.1686        | 14.2857 | 400  | 1.4481          | 46.3968 |
+| 0.0761        | 17.8571 | 500  | 1.3707          | 40.8808 |
+| 0.0452        | 21.4286 | 600  | 1.4051          | 38.5231 |
+| 0.0188        | 25.0    | 700  | 1.4044          | 36.7883 |
+| 0.0167        | 28.5714 | 800  | 1.4217          | 38.8345 |
+| 0.0084        | 32.1429 | 900  | 1.4120          | 48.5765 |
+| 0.0033        | 35.7143 | 1000 | 1.4151          | 39.2349 |
+| 0.0022        | 39.2857 | 1100 | 1.4401          | 39.7242 |
+| 0.0008        | 42.8571 | 1200 | 1.4591          | 39.5907 |
+| 0.0007        | 46.4286 | 1300 | 1.4679          | 39.5907 |
+| 0.0006        | 50.0    | 1400 | 1.4724          | 39.8577 |
+| 0.0007        | 53.5714 | 1500 | 1.4737          | 39.7242 |
 
-## Citation
 
-If you use this model, please consider citing the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430):
-```
-@misc{gandhi2023distilwhisper,
-      title={Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling}, 
-      author={Sanchit Gandhi and Patrick von Platen and Alexander M. Rush},
-      year={2023},
-      eprint={2311.00430},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-```
+### Framework versions
 
-## Acknowledgements
-* OpenAI for the Whisper [model](https://huggingface.co/openai/whisper-large-v2) and [original codebase](https://github.com/openai/whisper)
-* Hugging Face 🤗 [Transformers](https://github.com/huggingface/transformers) for the model integration
-* Google's [TPU Research Cloud (TRC)](https://sites.research.google/trc/about/) programme for Cloud TPU v4s
-* [`@rsonavane`](https://huggingface.co/rsonavane/distil-whisper-large-v2-8-ls) for releasing an early iteration of Distil-Whisper on the LibriSpeech dataset
+- Transformers 4.42.3
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

generation_config.json

@@ -85,6 +85,10 @@
   "decoder_start_token_id": 50257,
   "eos_token_id": 50256,
   "is_multilingual": false,
+  "lang_to_id": {
+    "<|en|>": 0
+  },
+  "language": "english",
   "max_initial_timestamp_index": 50,
   "max_length": 448,
   "no_timestamps_token_id": 50362,
@@ -183,6 +187,10 @@
     50360,
     50361
   ],
-  "transformers_version": "4.36.0.dev0",
+  "task": "transcribe",
+  "task_to_id": {
+    "transcribe": 0
+  },
+  "transformers_version": "4.42.3",
   "use_scan": false
 }

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